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This paper proposes a new paradigm for senti- ment analysis: translation from text documents to a set of sentiment units. The techniques of deep language analysis for machine translation are …...

This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of po- tentially pertinent information, including several fa- vorability measures for …...

AbstractSentiment analysis dealing with the identification and evaluation of opinions towards a topic, a company, or a product is an essential task within media analysis. It is used to study …...

. In some applications of opinion mining in text, it is important to distinguish what an author is talking about from the subjective stance towards the topic. Therefore, it needs …...

Sentiment analysis seeks to characterize opinionated or evaluative aspects of nat- ural language text. We suggest here that appraisal expression extraction should be viewed as a fundamental task in sentiment …...

. In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used …...

This paper presents an application of PageR- ank, a random-walk model originally de- vised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess …...

For the Affective Text task at SemEval- 2007, University Paris 7’s system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and …...

AbstractAutomatic sentiment analysis in texts has attracted considerable attention in recent years. Most of the approaches developed to classify texts or sentences as positive or negative rest on a very …...

AbstractThis paper presents OMINE, an opinion mining system which aims to identify concepts such as products and their attributes, and analyze their corresponding polarities. Our work pioneers at linking extracted …...

This paper studies sentiment analysis from the user-generated content on the Web. In particular, it focuses on mining opinions from comparative sentences, i.e., to determine which entities in a compari- …...

AbstractThis paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an …...

It is a challenging task to identify sentiment polarity of Chinese reviews because the re- sources for Chinese sentiment analysis are limited. Instead of leveraging only monolin- gual Chinese knowledge, …...

Polarity lexicons have been a valuable re- source for sentiment analysis and opinion mining. There are a number of such lexi- cal resources available, but it is often sub- optimal …...

We explore the adaptation of English resources and techniques for text sentiment analysis to a new language, Spanish. Our main focus is the modification of an existing English semantic orientation …...

Sentiment analysis often relies on a se- mantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lex- icons, but they tend …...

We describe a novel language-independent ap- proach to the task of determining the polarity, positive or negative, of the author’s opinion on a specific topic in natural language text. In …...

A wish is “a desire or hope for something to happen.” In December 2007, people from around the world offered up their wishes to be printed on confetti and dropped …...

This paper studies sentiment analysis of condi- tional sentences. The aim is to determine whether opinions expressed on different topics in a conditional sentence are positive, negative or neutral. Conditional …...

In this paper we propose a methodology for sen- timent analysis of figurative language which ap- plies Word Sense Disambiguation and, through an n-gram graph based method, assigns polar- ity …...

This paper investigates a new task, subjec- tivity word sense disambiguation (SWSD), which is to automatically determine which word instances in a corpus are being used with subjective senses, and …...

In this paper, the authors present a new ap- proach to sentence level sentiment analysis. The aim is to determine whether a sentence expresses a positive, negative or neutral sen- …...

For sentiment analysis, lexicons play an important role in many related tasks. In this paper, aiming to build Chinese emo- tion lexicons for public use, we adopted a graph-based algorithm …...

Dynamic sentiment ambiguous adjectives (DSAAs) like “large, small, high, low” pose a challenging task on sentiment analysis. This paper proposes a knowledge-based method to automatically determine the semantic orientation of …...

. In this paper, we propose a new linguistic approach for sentiment analysis of Korean. In order to overcome shortcomings of previous works confined to statistical methods, we make effective …...

AbstractWith the growing interest in opinion mining from web data, more works are focused on mining in English and Chinese reviews. Probing into the problem of product opinion mining, this …...

AbstractIn this paper, we propose GermanPolarityClues, a new publicly available lexical resource for sentiment analysis for the German language. While sentiment analysis and polarity classification has been extensively studied at …...

In this paper, we investigate how modeling content structure can benefit text analysis ap- plications such as extractive summarization and sentiment analysis. This follows the lin- guistic intuition that rich …...

. The development of the Web services lets many users easily provide their opinions recently. Automatic summarization of enormous sentiments has been expected. Intuitively, we can summarize a review with …...

AbstractIn recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between …...

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for …...

Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierar- chical relationships of products attributes is not fully utilized. (2) Reviews or …...

. This paper presents and analyzes the KOLON System, created to facilitate Ko- rean Natural Language Processing (KNLP) and to improve experimental results through the KOLON Ontology. It is currently …...

We present a system called AESOP that au- tomatically produces affect states associated with characters in a story. This research repre- sents a first step toward the automatic genera- tion …...

AbstractThe paper presents results of an experiment dealing with sentiment analysis of Croatian text from the domain of finance. The goal of the experiment was to design a system model …...

AbstractIn the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope …...

Mining sentiment from user generated content is a very important task in Natural Language Processing. An example of such content is threaded discussions which act as a very im- portant …...

Recently, Opinion Mining (OM) is receiving more at- tention due to the abundance of forums, blogs, e- commerce web sites, news reports and additional web sources where people tend to …...

In this paper, we introduce a connotation lex- icon, a new type of lexicon that lists words with connotative polarity, i.e., words with pos- itive connotation (e.g., award, promotion) and …...

Based on analysis of on-line review corpus we observe that most sentences have compli- cated opinion structures and they cannot be well represented by existing methods, such as frame-based and …...

AbstractSubjective language detection is one of the most important challenges in Sentiment Analysis. Because of the weight and frequency in opinionated texts, adjectives are considered a key piece in the …...

We investigate differences in point of view (POV) between two objective documents, where one is describing the subject matter in a more positive/negative way than the other, and present an …...

In this paper, we define a new type of summary for sentiment analysis: a single- sentence summary that consists of a sup- porting sentence that conveys the overall sentiment of …...

Together with the ever-growing amount of Chinese web data, the number of opinions voiced by Chinese users is rapidly increasing, and analyzing them is an important task. This paper intro- …...

Sentiment analysis of citations in scientific pa- pers and articles is a new and interesting prob- lem which can open up many exciting new ap- plications in bibliographic search and …...

AbstractTypically, accuracy is used to represent the performance of an NLP system. However, accuracy attainment is a function of investment in annotation. Typically, the more the amount and sophistication of …...

Public opinion analysis for micro-blog post is a new trend, and wherein emotional tendency analysis on micro-blog topic is a hot spot in the sentiment analysis. According to the characteristics …...

AbstractSentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Although much research has been performed in this area, most existing approaches are based on word-level …...

Automatic detection of negation cues along with their scope and corresponding negated events is an important task that could bene- fit other natural language processing (NLP) tasks such as extraction …...

AbstractExpressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to …...

We introduce the social study of bullying to the NLP community. Bullying, in both physi- cal and cyber worlds (the latter known as cy- berbullying), has been recognized as a …...

AbstractThere have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages …...

We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the …...

In recent years, statistical parsers have reached high performance levels on well-edited texts. Domain adaptation techniques have improved parsing results on text genres differing from the journalistic data most parsers …...

AbstractSentiment analysis, or opinion mining, is the process of extracting sentiment from documents or sentences, where the expressed sentiment is typically categorized as positive, negative, or neutral. Many different techniques …...

In this paper we explore the utility of sen- timent analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is moti- vated by …...

The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The short- ness of the length and the highly informal …...

This paper aims to seek approaches in investigating the relationships within emotion words under linguistic aspect, rather than figuring out new algorithms or so in processing emotion detection. It is …...

This paper briefly reports our system for the SemEval-2013 Task 2: sentiment analysis in Twitter. We first used an SVM classifier with a wide range of features, including bag of …...

Document-level sentiment analysis can bene- fit from fine-grained subjectivity, so that sen- timent polarity judgments are based on the relevant parts of the document. While fine- grained subjectivity annotations are …...

Explanatory sentences are employed to clarify reasons, details, facts, and so on. High quality online product reviews usually include not only positive or negative opinions, but also a variety of …...

Sentiment Analysis in Twitter has become an important task due to the huge user-generated content published over such media. Such analysis could be useful for many domains such as Marketing, …...

Different demographics, e.g., gender or age, can demonstrate substantial variation in their language use, particularly in informal contexts such as social media. In this paper we focus on learning gender …...

This paper presents the Tweetsted system im- plemented for the SemEval 2013 task on Sen- timent Analysis in Twitter. In particular, we participated in Task B on Message Polar- ity …...

This paper describes the details of our system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter. Our approach to predicting the sentiment of Tweets and SMS is …...

Sentiment analysis is currently a very dy- namic field in Computational Linguistics. Research herein has concentrated on the development of methods and resources for different types of texts and various …...

Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. How- ever, recent studies in this direction only con- sider direct …...

Most sentiment analysis approaches rely on machine-learning techniques, using a bag-of-words (BoW) document represen- tation as their basis. In this paper, we examine whether a more fine-grained rep- resentation of …...

We propose a novel approach to sentiment analysis for a low resource setting. The in- tuition behind this work is that sentiment expressed towards an entity, targeted senti- ment, may …...

Many sentiment-analysis methods for the classification of reviews use training and test-data based on star ratings provided by reviewers. However, when reading re- views it appears that the reviewers’ rat- …...

Assigning a positive or negative score to a word out of context (i.e. a word’s prior polar- ity) is a challenging task for sentiment analy- sis. In the literature, various …...

This paper describes the participation of the SINAI research group in the 2013 edi- tion of the International Workshop Se- mEval. The SINAI research group has submitted two systems, which …...

Sentiment analysis refers to automatically ex- tracting the sentiment present in a given natu- ral language text. We present our participation to the SemEval2013 competition, in the senti- ment analysis …...

For SemEval-2013 Task 2, A and B (Sen- timent Analysis in Twitter), we use a rule- based pattern matching system that is based on an existing ‘Domain Independent’ sentiment taxonomy …...

In this paper, we describe our system sub- mitted for the Sentiment Analysis task at Se- mEval 2013 (Task 2). We implemented a com- bination of Explicit Semantic Analysis (ESA) …...

In this work we present sentiment analysis of messag- es posted on a medical forum. We categorize posts, written in English, into five categories: encourage- ment, gratitude, confusion, facts, and …...

Most of the recent literature on Sentiment Analysis over Twitter is tied to the idea that the senti- ment is a function of an incoming tweet. However, tweets are filtered …...

AbstractSentiment analysis (or opinion mining) deals with the task of determining the polarity of an opinionated document or sentence. Users often express sentiment about one product by comparing it to …...

AbstractWe present a complete UIMA-based pipeline for sentiment analysis in Portuguese news using freely available resources and a minimal set of manually annotated training data. We obtained good precision on …...

Chinese radicals are linguistic elements smaller than Chinese characters1. Normally, a radical is a semantic category and almost all characters contain radicals or are radicals themselves. In subjectivity classification on …...

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the is- …...

The popularity of the user generated content, such as Twitter, has made it a rich source for the sentiment analysis and opinion min- ing tasks. This paper presents our study …...

AbstractAnnotation is an essential step in the development cycle of many Natural Language Processing (NLP) systems. Lately, crowd-sourcing has been employed to facilitate large scale annotation at a reduced cost. …...

Non-literal expression recognition is a challenging task in natural language processing. An ironic expression implies the opposite of the literal meaning, causing problems in opinion mining and sentiment analysis. In …...

Clustering aspect-related phrases in terms of product’s property is a precursor pro- cess to aspect-level sentiment analysis which is a central task in sentiment analy- sis. Most of existing methods …...

This paper describes the system we sub- mitted to the SemEval-2014 shared task on sentiment analysis in Twitter. Our sys- tem is a hybrid combination of two system developed for …...

In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical …...

This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most ex- isting machine learning approaches suf- fer from limitations in the modeling of …...

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec- tors for sentence-level classification tasks. We show that a simple CNN …...

AbstractIn computation linguistics a combination of syntagmatic and paradigmatic features is often exploited. While the first aspects are typically managed by information present in large n-gram databases, domain and ontological …...

Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies …...

Microblogging websites (such as Twitter, Facebook) are rich sources of data for opinion mining and sentiment analysis. In this paper, we describe our approaches used for sentiment analysis in twitter …...

This paper introduces a model of multiple- instance learning applied to the predic- tion of aspect ratings or judgments of specific properties of an item from user- contributed texts such …...

AbstractPerson-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social …...

AbstractWe introduce GraPAT, a web-based annotation tool for building graph structures over text. Graphs have been demonstrated to be relevant in a variety of quite diverse annotation efforts and in …...

Sentiment Analysis has become an in- creasingly important research topic. This paper describes our approach to building a system for the Sentiment Analysis in Twit- ter task of the SemEval-2014 …...

AbstractSentiment analysis is genre and domain dependent, i.e. the same method performs differently when applied to text that originates from different genres and domains. Intuitively, this is due to different …...

This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and …...

Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentimen- t analysis. This paper aims at extract- …...

AbstractThe NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies. …...

AbstractIn this paper, we describe a publicly available multilingual evaluation corpus for phrase-level Sentiment Analysis that can be used to evaluate real world applications in an industrial context. This corpus …...

This paper describes an approach for political tendency identification of Twitter users. We define some metrics that take into account the polarity of the political entities in the tweets of …...

Recently the research on supervised term weighting has attracted growing attention in the field of Tradi- tional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show …...

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of …...

Sentiment analysis has become an important classification task because a large amount of user-generated content is published over the Internet. Sentiment lexicons have been used successfully to classify the sentiment …...

Opinions may be expressed implicitly via inference over explicit sentiments and events that positively/negatively affect en- tities (goodFor/badFor events). We in- vestigate how such inferences may be exploited to improve …...

AbstractIn this paper we present SenTube – a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers …...

This article presents the participation of the SINAI research group in the task Sen- timent Analysis in Twitter of the SemEval Workshop. Our proposal consists of a voting system of …...

AbstractSarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but …...

We report on interoperability of different sentiment lexica with each other and with the linguistic notions negation and modal- ity for sentiment analysis of tweets in a comprehensive ablation study …...

Semantic role labeling has become a key mod- ule for many language processing applica- tions such as question answering, information extraction, sentiment analysis, and machine translation. To build an unrestricted …...

In this paper we present an approach for anal- ysis of sentiments and emotions in image tag- ging using SentiWordNet as an external lin- guistic resource of emotional words. Our …...

When applying machine learning to prob- lems in NLP, there are many choices to make about how to represent input texts. They can have a big effect on perfor- mance, …...

Discourse structure is the hidden link be- tween surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers …...

Sentiment analysis from a text requires amongst others having a polarity lexical resource. We designed LikeIt, a GWAP (Game With A Purpose) that allows to at- tribute a positive, negative …...

This paper describes the details of our sys- tem submitted to the SemEval 2015 shared task on sentiment analysis of figurative lan- guage on Twitter. We tackle the problem as …...

In November 2014, the European Cen- tral Bank (ECB) started to directly su- pervise the largest banks in the Euro- zone via the Single Supervisory Mecha- nism (SSM). While supervisory …...

This paper proposes a tree-based con- volutional neural network (TBCNN) for discriminative sentence modeling. Our model leverages either constituency trees or dependency trees of sentences. The tree-based convolution process extracts …...

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod- els of vector-space representations. Yet while ‘multi-sense’ methods have been proposed …...

Most of the current approaches to sentiment analysis of product reviews are dependent on lexical sentiment information and proceed in a bottom-up way, adding new layers of features to lexical …...

News reports, social media streams, blogs, digitized archives and books are part of a plethora of reading sources that people face every day. This raises the question of how to …...

This paper proposes a novel approach to sen- timent analysis that leverages work in soci- ology on symbolic interactionism. The pro- posed approach uses Affect Control Theory (ACT) to analyze …...

In this paper we present our solution for ob- taining sentiment at message-level of short sentences. The system combines the use of polarity dictionaries and Conditional Random Fields to obtain …...

This paper describes an approach to large- scale modeling of sentiment analysis for the social sciences. The goal is to model relations between nation states through so- cial media. Many …...

We present the INESC-ID system for the mes- sage polarity classification task of SemEval 2015. The proposed system does not make use of any hand-coded features or linguistic resources. It …...

Wikipedia is supposed to be supporting the “Neutral Point of View”. Instead of accept- ing this statement as a fact, the current paper analyses its veracity by specifically analysing a …...

In this work, we build an entity/event-level sentiment analysis system, which is able to recognize and infer both explicit and im- plicit sentiments toward entities and events in the text. …...

In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval- 2015 Task …...

Our current work analyses relations between sentiments and activity of authors of online In- Vitro Fertilization forums. We focus on two types of active authors: those who start new discussions …...

The vast information related to products and services available online, of both ob- jective and subjective nature, can be used to provide contextualized suggestions and guidance to possible new customers. …...

This paper describes our sentiment analysis systems which have been built for SemEval- 2015 Task 10 Subtask B and E. For sub- task B, a Logistic Regression classifier has been …...

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We consid- ered two approaches, classification and regres- sion, to provide fine-grained …...

Opinion summarization is the task of pro- ducing the summary of a text, such that the summary also preserves the sentiment of the text. Opinion Summarization is thus a trade-off …...

This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which tar- geted real-life Web forums, and was offered in both Arabic and English. We …...

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analy- sis in Twitter. This was the most popular sen- timent analysis shared task to …...

Sentiment analysis tends to focus on the po- larity of words, combining their values to de- tect which portion of a text is opinionated. CLIPEval wants to promote a more …...

When text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, …...

We present work on sentiment analysis in Twitter for Macedonian. As this is pio- neering work for this combination of lan- guage and genre, we created suitable re- sources for …...

Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional language to increase the ex- pressiveness. This task becomes even more difficult when people use figurative …...

Web reviews have been intensively studied in argumentation-related tasks such as sen- timent analysis. However, due to their fo- cus on content-based features, many sen- timent analysis approaches are effective …...

Sentiment analysis has been a major area of interest, for which the existence of high- quality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but …...

Sentiment analysis has undergone a shift from document-level analysis, where labels ex- presses the sentiment of a whole document or whole sentence, to subsentential approaches, which assess the contribution of …...

We present a novel word level vector rep- resentation based on symmetric patterns (SPs). For this aim we automatically ac- quire SPs (e.g., “X and Y”) from a large corpus …...

Sentiment analysis models often use rat- ings as labels, assuming that these rat- ings reflect the sentiment of the accom- panying text. We investigate (i) whether human readers can infer …...

We present an approach for tackling the Sen- timent Analysis problem in SemEval 2015. The approach is based on the use of a co- occurrence graph to represent existing rela- …...

Microblogs are considered as We-Media infor- mation with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two …...

AbstractSarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively …...

AbstractIn this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim …...

AbstractIn this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural …...

AbstractClassifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target …...

AbstractSentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts. Many approaches in literature are based on resources that model the prior polarity of words or …...

Sentiment Analysis (SA) is an active research area nowadays due to the tremendous interest in aggregating and evaluating opinions being disseminated by users on the Web. SA of English has …...

This paper describes the system with which we participated in SemEval-2016 Task 4 (Sen- timent Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted …...

This paper describes a sentiment analysis sys- tem developed by the bunji team in SemEval- 2016 Task 5. In this task, we estimate the sentimental polarity of a given entity-attribute …...

Access to word–sentiment associations is use- ful for many applications, including senti- ment analysis, stance detection, and linguistic analysis. However, manually assigning fine- grained sentiment association scores to words has …...

. Sentiment analysis is an emerging research field. One of the major tasks of sentiment analysis is building sentiment lexicons and calculating their scores, which is an essential job that …...

Sentiment lexicons have been leveraged as a useful source of features for sentiment anal- ysis models, leading to the state-of-the-art accuracies. On the other hand, most ex- isting methods use …...

AbstractRecent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per …...

AbstractThe present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. More specifically, videolectures, …...

This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Message Polarity Classifi- cation), B …...

AbstractThis paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done …...

This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment and participate in …...

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for …...

Minions, a team formed of first year stu- dents in the Master of Computational Lin- guistics, started the participation at Semeval-2016 as a semester project, aiming to build a model …...

AbstractRecognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its …...

AbstractWe present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec’s features, paragraph vector (batch and online) …...

AbstractThis paper presents NileULex, which is an Arabic sentiment lexicon containing close to six thousands Arabic words and compound phrases. Forty five percent of the terms and expressions in the …...

In many areas, such as social science, politics or market research, people need to track sentiment and their changes over time. For sentiment analysis in this field it is more …...

The paper describes experiments on sentiment classification of microblog messages using an architecture allowing general machine learn- ing classifiers to be combined either sequen- tially to form a multi-step classifier, …...

AbstractThis paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the …...

AbstractAn important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. …...

AbstractIn this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network …...

This paper describes our sentiment analysis system which has been built for Sentiment Analysis in Twitter Task of SemEval-2016. We have used a Logistic Regression classifier with different groups of …...

AbstractRecurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN …...

AbstractSentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area …...

AbstractDiscourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse relations. …...

The paper describes our submission to the task on Sentiment Analysis on Twitter at SemEval 2016. The approach is based on a Deep Learning architecture using convolu- tional neural networks. …...

The aim of this paper is to produce a method- ology for analyzing sentiments of selected Twitter messages, better known as Tweets. This project elaborates on two experiments carried out …...

AbstractSentiment analysis is the computational task of extracting sentiment from a text document – for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced …...

AbstractAttention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In …...

AbstractSentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usually …...

AbstractDomain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may …...

AbstractManual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to …...

AbstractTraditional learning-based approaches to sentiment analysis of written text use the concept of bag-of-words or bag-of-n-grams, where a document is viewed as a set of terms or short combinations of …...

AbstractThis paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect …...

AbstractIn this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and …...

AbstractUnknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their …...

AbstractThe study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the …...

AbstractIn this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural …...

AbstractToday’s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment …...

AbstractWe pose the general task of user-factor adaptation – adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece …...

AbstractIn this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory …...

AbstractAutomatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents …...

AbstractDomain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. …...

We improved sentiment classifier for predicting document-level sentiments from Twitter by using multi-channel lexicon embedidngs. The core of the architecture is based on CNN- BiLSTM that can capture high level …...

AbstractThe sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow …...

AbstractIn many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment …...

AbstractThis paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm. Specifically, this algorithm learns a word embedding matrix in tandem with …...

AbstractVariation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. …...

AbstractWe propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by …...

AbstractWord embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically …...

AbstractSentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions …...

AbstractThis paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets …...

AbstractFigurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits …...

. Valence shifters are complex linguistic structures that can modify the sentiment orientations of texts. In this paper, the authors concentrate on the study of shifters in Vietnamese texts and …...

AbstractIn this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et …...

AbstractVolatility prediction—an essential concept in financial markets—has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We …...

AbstractSentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences …...

Transcripts of UK parliamentary debates provide access to the opinions of politicians towards many important topics, but due to the large quantity of textual data and the specialised language used, …...

AbstractAttention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, …...

AbstractHuman communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and …...

Estimating the intensity of emotion has gained significance as modern textual inputs in po- tential applications like social media, retail markets, psychology, advertisementse- etc., carry a lot of emotions, feelings, …...

AbstractReview text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the abstractive review summarization that is essential …...

Social media platforms such as Twitter and Facebook are hugely popular websites through which Internet users can communicate and spread information worldwide. On Twitter, mes- sages (tweets) are generated by …...

AbstractThis paper discusses on task 1, “Affect in Tweets” sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based …...

Large amount of user-generated data are posted online in social media platforms, in- cluding user preferences, dining and leisure activities, events, news and personal blogs. This resulted in varying efforts …...

AbstractWe introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across …...

Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably …...

Several datasets of opinions expressed by Social networks’ users have been created to explore Sentiment Analysis tasks like Sentiment Polarity and Emotion Mining. Most of these datasets are focused on …...

AbstractWe study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a …...

This paper proposes a new method of instance-based domain adaptation for senti- ment analysis. First, our method defines the likelihood of keywords, through the value of inverse document frequency (IDF), …...

AbstractLexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity …...

AbstractAutomatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark …...

There is an increasing demand for multilingual sentiment analysis, and most work on sentiment lexicons is still carried out based on English lexicons like WordNet. In addition, many of the …...

AbstractDespite their superior performance, deep learning models often lack interpretability. In this paper, we explore the modeling of insightful relations between words, in order to understand and enhance predictions. To …...

The sentiment polarity of a phrase does not only depend on the polarities of its words, but also on how these are affected by their context. Negation words (e.g. not, …...

AbstractTraining data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on …...

AbstractIn this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level …...

AbstractSentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases …...

We present Lingmotif-lex, a new, wide-coverage, domain-neutral lexicon for sentiment analysis in English. We describe the creation process of this resource, its assumptions, format, and valence system. Unlike most sentiment …...

In this paper, we present our process to establish a PICO and a sentiment annotated corpus of clinical trial publications. PICO stands for Population, Intervention, Comparison and Outcome — these …...

AbstractBecause of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened …...

This paper describes a language-independent model for multi-class sentiment analysis using a simple neural network architecture of five layers (Embedding, Conv1D, GlobalMaxPooling and two Fully-Connected). The advantage of the proposed …...

One of the major challenges in the field of Natural Language Processing (NLP) is the handling of idioms; seemingly ordinary phrases which could be further conjugated or even spread across …...

This paper presents the Norwegian Review Corpus (NoReC), created for training and evaluating models for document-level sentiment analysis. The full-text reviews have been collected from major Norwegian news sources and …...

AbstractDomain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are …...

In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral). However, these richer formats (like Basic Emotions …...

Understanding the polarity or sentiment of a text is an important task in many application scenarios. Sentiment Analysis of a text can be used to answer various questions such as …...

Like many social variables, gender pervasively influences how people communicate with one another. However, prior computational work has largely focused on linguistic gender difference and communications about gender, rather than …...

AbstractThis paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. …...

Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification (i.e., extracting the target of ridicule in a …...

The natural language processing subfield known as sentiment analysis or opinion mining has seen an explosive expansion over the last decade or so, and sentiment analysis has become a standard …...

Sentiment analysis has been receiving increasing interest as it conveys valuable information in regard to people’s preferences and opinions. In this work, we present a sentiment analyzer that identifies the …...

In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word …...

AbstractLearning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive …...

AbstractStandard word embedding algorithms learn vector representations from large corpora of text documents in an unsupervised fashion. However, the quality of word embeddings learned from these algorithms is affected by …...

AbstractRecurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. …...

Sentence Classification is one of the most fun- damental tasks in NLP, where the aim is to classify a given sentence into a pre-defined set of classes. A lot of …...

AbstractEmotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and …...

This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted …...

AbstractTraditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can’t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is …...

The ability to analyze sentiment is a ma- jor technology to analyze social media pro- cess. The sentiment analysis involves read- ing and understanding what is being said about a …...

AbstractPredicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has …...

AbstractEmotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these …...

AbstractState-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of possible transformations scales exponentially with text length, …...

AbstractSentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of …...

AbstractWe present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce …...

AbstractIn this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT. In particular, sparsity …...

AbstractWe use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title’s …...

AbstractSocial media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization …...

AbstractSocial media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature …...

AbstractContextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, …...

AbstractTweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural …...

AbstractDeep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the …...

AbstractNegation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are …...

AbstractIncorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a …...

AbstractThere has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a …...

AbstractRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event …...

AbstractThere exist biases in individual’s language use; the same word (e.g., cool) is used for expressing different meanings (e.g., temperature range) or different words (e.g., cloudy, hazy) are used for …...

AbstractRelated tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs …...

AbstractWe present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages. We accomplish this by inducing a mapping from …...

AbstractSentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge …...

AbstractThe goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text …...

AbstractSupervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance. To solve the problem, training data selection (TDS) has …...

AbstractIn this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper’s contributions include a method for handling any sized input and …...

AbstractFake reviews are increasingly prevalent across the Internet. They can be unethical as well as harmful. They can affect businesses and mislead individual customers. As the opinions on the Web …...

AbstractText classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, …...

AbstractMany NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence …...

AbstractArabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a …...

AbstractText classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this …...

AbstractSentiment analysis in low-resource languages suffers from the lack of training data. Cross-lingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other …...

AbstractTree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only …...

AbstractThe quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during …...

AbstractThis paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning. We analyze the syntax, abbreviations, and informal-writing of Twitter; and perform perfect data …...

AbstractAffective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a …...

AbstractIn this paper, we aim at learning the relationships and similarities of a variety of tasks, such as humour detection, sarcasm detection, offensive content detection, motivational content detection and sentiment …...

AbstractIn Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as “positive”, “neutral”, “negative” in sentiment analysis. Remarkably, the most popular evaluation metrics …...

AbstractParody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this …...

AbstractGiven the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences …...

AbstractArabizi is a written form of spoken Arabic, relying on Latin characters and digits. It is informal and does not follow any conventional rules, raising many NLP challenges. In particular, …...

AbstractNeural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on robustness …...

AbstractSentiment analysis, being one of the most sought after research problems within Natural Language Processing (NLP) researchers. The range of problems being addressed by sentiment analysis is increasing. Till now, …...

AbstractIn today’s interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well …...

AbstractWe present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition …...

AbstractWe present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared …...

AbstractOne of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. …...

AbstractThere has been an increasing interest in learning cross-lingual word embeddings to transfer knowledge obtained from a resource-rich language, such as English, to lower-resource languages for which annotated data is …...

AbstractThe growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching. While …...

AbstractIn recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the internet. It has various applications like opinion mining, social media …...

AbstractSentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies. Although supervised learning algorithms have advanced considerably in recent years, …...

Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facili- tate several use cases such as legal judgement prediction, contradictory statements identi- fication and party-based sentiment analysis. …...

AbstractExisting methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the …...

AbstractWe present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply …...

AbstractDespite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks …...

AbstractThis paper presents the study of sentiment analysis for Amharic social media texts. As the number of social media users is ever-increasing, social media platforms would like to understand the …...

AbstractThe recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a …...

AbstractCode-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on …...

AbstractGradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, the …...

AbstractThe phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing. This is more evident in multilingual societies. In this paper, we have …...

AbstractSentiment analysis for code-mixed social media text continues to be an under-explored area. This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT. Prior …...

AbstractWe present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36,000 code-switched …...

AbstractSocial media is abundant in visual and textual information presented together or in isolation. Memes are the most popular form, belonging to the former class. In this paper, we present …...

AbstractTweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to …...

AbstractRecent Transformer-based contextual word representations, including BERT and XLNet, have shown state-of-the-art performance in multiple disciplines within NLP. Fine-tuning the trained contextual models on task-specific datasets has been the key …...

AbstractTo demystify the “black box” property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction …...

AbstractIrony is a linguistic device used to intend an idea while articulating an opposing expression. Many text analytic algorithms used for emotion extraction or sentiment analysis, produce invalid results due …...

AbstractUniversal embeddings, such as BERT or ELMo, are useful for a broad set of natural language processing tasks like text classification or sentiment analysis. Moreover, specialized embeddings also exist for …...

AbstractSentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with …...

AbstractEnd-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, …...

AbstractCode-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic …...

AbstractSentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages. However, these methods rely heavily on large amounts of labeled data …...

AbstractRecent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. …...

AbstractSentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may …...

AbstractThe recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper …...

AbstractOver the recent years, Arabic language resources and NLP tools have been under rapid development. One of the important tasks for Arabic natural language processing is the sentiment analysis. While …...

AbstractIn this paper, we present our system for the SemEval 2020 task on code-mixed sentiment analysis. Our system makes use of large transformer based multilingual embeddings like mBERT. Recent work …...

AbstractDue to the phenomenal growth of online content in recent time, sentiment analysis has attracted attention of the researchers and developers. A number of benchmark annotated corpora are available for …...

AbstractUsers express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior …...

AbstractThe human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on …...

AbstractSocial media sites like Twitter possess the potential to complement surveys that measure political opinions and, more specifically, political actors’ approval. However, new challenges related to the reliability and validity …...

AbstractDebate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for people to process manually. While sentiment analysis of debate …...

AbstractThis paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT …...

AbstractWe explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main …...

AbstractDeep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP). These models learn contextualized representations by using a huge amount of unlabeled text data …...

AbstractMost of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in …...

AbstractIn this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, and thereby propose a multi-task deep learning framework to solve all these three problems simultaneously in …...

AbstractPre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users’ emotional expressions across domains, fine-tuning the …...

AbstractWithin this work we describe a framework for the collection and summarization of information from the Web in an entity-driven manner. The framework consists of a set of appropriate workflows …...

AbstractAdversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can …...

AbstractContextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years. Extensive work shows …...

AbstractRecently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks. However, since the pre-training stage is typically task-agnostic and the fine-tuning stage …...

AbstractThe experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony …...

AbstractThis paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and …...

AbstractWe introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new …...

AbstractThe streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance …...

AbstractThe existing research on sentiment analysis mainly utilized data curated in limited geographical regions and demography (e.g., USA, UK, China) due to commercial interest and availability of review data. Since …...

AbstractImposing the style of one image onto another is called style transfer. For example, the style of a Van Gogh painting might be imposed on a photograph to yield an …...

This study applied sentiment analysis to the corpus data and analyzes the men’s and women’s speech. For the analysis, the BNC64 corpus was used and three different types of analyses …...

AbstractSentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction …...

AbstractRecent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on …...

AbstractWhen a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of …...

AbstractMuch work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures …...

AbstractExisting audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, …...

Sentiment lexicons are instrumental for senti- ment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment analysis. One …...

AbstractFinetuning deep pre-trained language models has shown state-of-the-art performances on a wide range of Natural Language Processing (NLP) applications. Nevertheless, their generalization performance drops under domain shift. In the case …...

We introduce categorical modularity, a novel low-resource intrinsic metric to evaluate word embedding quality. Categorical modularity is a graph modularity metric based on the k-near- est neighbor graph constructed with …...

AbstractPrevious work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only …...

Although sentiment is conceptualized as a con- tinuous variable, most text-based sentiment analyses categorize texts into discrete senti- ment categories. Compared to discrete catego- rizations, continuous sentiment estimates pro- vide …...

AbstractIn this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model …...

AbstractModelling a word’s polarity in different contexts is a key task in sentiment analysis. Previous works mainly focus on domain dependencies, and assume words’ sentiments are invariant within a specific …...

AbstractAspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing …...

AbstractFine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is …...

How much information do NLP tasks really need from a transformer’s attention mecha- nism at application-time (inference)? From recent work, we know that there is sparsity in transformers and that …...

AbstractOpinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. …...

Do state-of-the-art natural language under- standing models care about word order? Not always! We found 75% to 90% of the correct predictions of BERT-based classifiers, trained on many GLUE tasks, …...

AbstractWe investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in …...

AbstractBackdoor attacks, which maliciously control a well-trained model’s outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusing deep neural networks …...

AbstractSentiment analysis has become a popular research issue in recent years, especially on educational texts which is an important problem. According to literature, the similar sentence generation can help the …...

AbstractMachine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment and emotion and gender traits and etc.) to the target and …...

AbstractThe explosion of user-generated content (UGC)—e.g. social media posts and comments and and reviews—has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these …...

AbstractSifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health …...

Significant progress in sentiment analysis and emotion recognition has been made recently, and there has also been an increase in the re- quirements for solving real-world problems. However, human language …...

AbstractSentiment analysis systems have been shown to exhibit sensitivity to protected attributes. Round-trip translation, on the other hand, has been shown to normalize text. We explore the impact of round-trip …...

Recent years have seen a rise in interest for cross-lingual transfer between languages with similar typology, and between languages of various scripts. However, the interplay be- tween language similarity and …...

AbstractSpeech Act Classification determining the communicative intent of an utterance has been investigated widely over the years as a standalone task. This holds true for discussion in any fora including …...

AbstractIn writing, humor is mainly based on figurative language in which words and expressions change their conventional meaning to refer to something without saying it directly. This flip in the …...

Adapting a model to a handful of personal- ized data is challenging, especially when it has gigantic parameters, such as a Transformer- based pretrained model. The standard way of fine-tuning …...

AbstractWith the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research …...

AbstractEver-expanding evaluative texts on online forums have become an important source of sentiment analysis. This paper proposes an aspect-based annotated dataset consisting of telecom reviews on social media. We introduce …...

In political news media, propaganda techniques are often employed to express one’s political view, or to influence the audience’s stance. Chinese corpora with the  國立清華大學臺灣語言研究與教學研究所；國立中正大學通識教育中心 Institute of Taiwan Languages …...

AbstractPeer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often …...

AbstractWhile there is increasing concern about the interpretability of neural models, the evaluation of interpretability remains an open problem, due to the lack of proper evaluation datasets and metrics. In …...

AbstractFor research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make …...

AbstractWord embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) do not capture the affective dimensions of valence, arousal, and dominance, which are present inherently in words. We present a …...

AbstractRecently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation …...

AbstractCode-mixing refers to the mixed use of multiple languages. It is prevalent in multilingual societies and is also one of the most challenging natural language processing tasks. In this paper, …...

AbstractThis paper introduces our Diversity Advanced Actor-Critic reinforcement learning (A2C) framework (DAAC) to improve the generalization and accuracy of Natural Language Processing (NLP). We show that the diversification of training …...

AbstractDespite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. It is because they may latch onto spurious correlations (“shortcuts”, e.g., focusing only on …...

AbstractUnderstanding the needs and fears of citizens, especially during a pandemic such as COVID-19, is essential for any government or legislative entity. An effective COVID-19 strategy further requires that the …...

AbstractWe investigated the influence of contradictory connotations of words or phrases occurring in sarcastic statements, causing those statements to convey the opposite of their literal meaning. Our approach was to …...

AbstractLarge pre-trained language models (LMs) have demonstrated the ability to obtain good performance on downstream tasks with limited examples in cross-lingual settings. However, this was mostly studied for relatively resource-rich …...

AbstractFinding the polarity of feelings in texts is a far-reaching task. Whilst the field of natural language processing has established sentiment analysis as an alluring problem, many feelings are left …...

AbstractSince the division of Korea, the two Korean languages have diverged significantly over the last 70 years. However, due to the lack of linguistic source of the North Korean language, …...

AbstractRunning large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing …...

AbstractSentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. In this paper, we focus on the structured sentiment analysis task that is …...

AbstractWord embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) are good at encoding various lexical-semantic relations. However, they do not capture the emotion aspects of words. We present a …...

AbstractThis paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document - what we refer to as Entity-Level Sentiment Analysis (ELSA). …...

AbstractThe stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, …...

AbstractCurrent approaches for fixing systematic problems in NLP models (e.g., regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In contrast, humans often provide …...

AbstractThe quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises, …...

AbstractGRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language. The overall goal of building an ontology of rhetorical figures in German is …...

AbstractSocial media platforms such as Twitter have evolved into a vast information sharing platform, allowing people from a variety of backgrounds and expertise to share their opinions on numerous events …...

AbstractStance detection aims to identify people’s standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks. Recent studies have proposed multi-task learning models …...

AbstractSentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there …...

AbstractNegation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we …...

AbstractWith the growing popularity of deep-learning models, model understanding becomes more important. Much effort has been devoted to demystify deep neural networks for better explainability. Some feature attribution methods have …...

AbstractThe quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures …...

AbstractUser-generated content is full of misspellings. Rather than being just random noise, we hypothesise that many misspellings contain hidden semantics that can be leveraged for language understanding tasks. This paper …...

AbstractThe interaction between a consumer and the customer service representative greatly contributes to the overall customer experience. Therefore, to ensure customers’ comfort and retention, it is important that customer service …...

AbstractPrompt-based learning (a.k.a. prompting) achieves high performance by bridging the gap between the objectives of language modeling and downstream tasks. Domain generalization ability can be improved by prompting since classification …...

AbstractSeveral existing resources are available for sentiment analysis (SA) tasks that are used for learning sentiment specific embedding (SSE) representations. These resources are either large, common-sense knowledge graphs (KG) that …...

AbstractThis paper investigates the sentiment analysis task from a novel perspective by incorporating sentiment knowledge and eye movement into a graph architecture, aiming to draw the eye movement-based sentiment relationships …...

AbstractUser language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their data. In this …...

AbstractIn recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, …...

We present the results of a project perform- ing sentiment analysis on tweets from Ger- man politicians and party accounts for the 2021 German federal election. We collected over 58,000 …...

AbstractMost existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose …...

In the process of numerically modeling natural languages, developing language embeddings is a vital step. However, it is challenging to develop functional embeddings for resource- poor languages such as Sinhala, …...

AbstractSentiment analysis is a useful problem which could serve a variety of fields from business intelligence to social studies and even health studies. Using SemEval 2022 Task 10 formulation of …...

AbstractSentiment analysis in social media is challenging since posts are short of context. As a popular way to express emotion on social media, stickers related to these posts can supplement …...

AbstractThe aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence. The development of the ABSA task …...

AbstractSarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of …...

AbstractWe propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text …...

AbstractThis study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable …...

AbstractWords play a central role in how we express ourselves. Lexicons of word–emotion associations are widely used in research and real-world applications for sentiment analysis, tracking emotions associated with products …...

AbstractWe present bgGLUE (Bulgarian General Language Understanding Evaluation), a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. Our benchmark includes NLU tasks targeting a variety …...

AbstractSentiment Analysis is an aspect of natural languageprocessing (NLP) that has been a topicof research. While most studies focus on highresourcelanguages with an extensive amountof available data, the study on …...

AbstractWe study the out-of-distribution generalization of active learning that adaptively selects samples for annotation in learning the decision boundary of classification. Our empirical study finds that increasingly annotating seen samples …...

While argument mining has garnered attention over the years, its application in the financial sector remains nascent. This study presents a BERT-based ensemble learning approach tailored for sentiment analysis grounded …...

AbstractThis paper presents our proposed method for SemEval-2023 Task 12, which focuses on sentiment analysis for low-resource African languages. Our method utilizes a language-centric domain adaptation approach which is based …...

AbstractThis report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, …...

AbstractPretrained models are a mainstay in modern NLP applications. Pretraining requires access to large volumes of unlabeled text. While monolingual text is readily available for many of the world’s languages, …...

Abstract“Sentiment analysis is a crucial text classification task that aims to extract, process, and analyzeopinions, sentiments, and subjectivity within texts. In current research on Chinese text, sentenceand aspect-based sentiment analysis …...

AbstractModel explanations that shed light on the model’s predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy …...

AbstractWith the prominence of large pretrained language models, low-resource languages are rarely modelled monolingually and become victims of the “curse of multilinguality” in massively multilingual models. Recently, AfriBERTa showed that …...

AbstractLittle attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper …...

AbstractThis paper describes our system developed for the SemEval-2023 Task 12 “Sentiment Analysis for Low-resource African Languages using Twitter Dataset”. Sentiment analysis is one of the most widely studied applications …...

AbstractOne of the most extensively researched applications in natural language processing (NLP) is sentiment analysis. While the majority of the study focuses on high-resource languages (e.g., English), this research will …...

AbstractNLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language …...

AbstractSentiment analysis has become a central tool in various disciplines outside of natural language processing. In particular in applied and domain-specific settings with strong requirements for interpretable methods, dictionary-based approaches …...

AbstractThe Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several …...

AbstractIn this paper, we have performed sentiment analysis on three African languages (Hausa, Swahili, and Yoruba). We used various deep learning and traditional models paired with a vectorizer for classification …...

AbstractWe describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual …...

AbstractWe propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, …...

AbstractMasked language modeling (MLM) plays a key role in pretraining large language models. But the MLM objective is often dominated by high-frequency words that are sub-optimal for learning factual knowledge. …...

AbstractMulti-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, …...