Сезімталдықты Жіктеу

We consider the problem of classifying doc- uments not by topic, but by overall senti- ment, e.g., determining whether a review is positive or negative. Using movie re- views as …...

We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination …...

. Traditional text categorization is usually a topic-based task, but a subtle demand on information retrieval is to distinguish between positive and negative view on text topic. In this paper, …...

Recent studies of sentiment classification (determining whether a text is “positive” or “negative”) using Appraisal theory have provided mixed results. While some good results have been obtained, it is difficult …...

Recent systems have been developed for sentiment classification, opinion recogni- tion, and opinion analysis (e.g., detect- ing polarity and strength). We pursue an- other aspect of opinion analysis: identi- fying …...

We propose a new framework for supervised ma- chine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: …...

NLP tasks are often domain specific, yet sys- tems can learn behaviors across multiple do- mains. We develop a new multi-domain online learning framework based on parameter com- bination from …...

We address the problem of sentiment and objectivity classification of product re- views in Chinese. Our approach is distinct- ive in that it treats both positive / negative sentiment and …...

. Negation and contrast transition are two kinds of linguistic phenomena which are popularly used to reverse the sentiment polarity of some words and sentences. In this paper, we propose …...

In this paper, we present a dependency tree- based method for sentiment classification of Japanese and English subjective sentences us- ing conditional random fields with hidden variables. Subjective sentences often …...

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 adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised sentiment classification. Here, personal views consist of those sentences which …...

In this work, we propose a novel representa- tion of text based on patterns derived from lin- guistic annotation graphs. We use a subgraph mining algorithm to automatically derive fea- …...

AbstractIn this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a …...

This paper explores approaches to sentiment classification of U.S. Congressional floor- debate transcripts. Collective classification techniques are used to take advantage of the informal citation structure present in the de- …...

We consider a semi-supervised setting for do- main adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative …...

Traditional approaches to sentiment classifica- tion rely on lexical features, syntax-based fea- tures or a combination of the two. We pro- pose semantic features using word senses for a supervised …...

. With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, …...

We describe a sentiment classification method that is applicable when we do not have any la- beled data for a target domain but have some labeled data for multiple other …...

Active learning is a promising way for sentiment classification to reduce the annotation cost. In this paper, we focus on the imbalanced class distribution scenario for sentiment classification, wherein the …...

Research in text classification currently fo- cuses on challenging tasks such as sen- timent classification, modality identifica- tion, and so on. In these tasks, approaches that use a structural representation, …...

Using labeled Twitter training data from SemEval-2013, we train both a subjectivity classifier and a polarity classifier separately, and then combine the two into a single hier- archical classifier. Using …...

The paper describes experiments using grid searches over various combinations of ma- chine learning algorithms, features and pre- processing strategies in order to produce the optimal systems for sentiment classification …...

We evaluate a naive machine learning ap- proach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a classifier based on …...

The lack of labeled data always poses challenges for tasks where machine learn- ing is involved. Semi-supervised and cross-domain approaches represent the most common ways to overcome this dif- ficulty. …...

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- …...

Expensive feature engineering based on WordNet senses has been shown be useful for document level sentimentto classification. A plausible reason for such a performance improvement is the reduction in data …...

In this paper, we describe the development and performance of the supervised system UMCC_DLSI-(SA). This system uses corpora where phrases are annotated as Positive, Negative, Objective, and Neutral, to achieve …...

In this paper, we develop a novel semi-supervised learning algorithm called hybrid deep be- lief networks (HDBN), to address the semi-supervised sentiment classification problem with deep learning. First, we construct …...

The shared task on Aspect based Senti- ment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this …...

AbstractSentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove …...

We present a sentiment classification sys- tem that participated in the SemEval 2014 shared task on sentiment analysis in Twit- ter. Our system expands tokens in a tweet with semantically …...

This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective …...

Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. How- ever, those approaches incur high costs in the making of dictionaries and/or features, which …...

The success of deep learning often de- rives from well-chosen operational build- ing blocks. In this work, we revise the temporal convolution operation in CNNs to better adapt it to …...

For sentiment classification, it is often recognized that embedding based on dis- tributional hypothesis is weak in captur- ing sentiment contrast–contrasting words may have similar local context. Based on broader …...

In this paper, we propose a baseline message- level sentiment classification method, as de- veloped for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding …...

Document sentiment classification is of- ten processed by applying machine learn- ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train- ing the …...

This paper describes our sentiment classifica- tion system submitted to SemEval-2015 Task 10. In the message-level polarity classifica- tion subtask, we obtain the highest macro- averaged F1-scores on three out …...

This paper describes a sentiment classifica- tion system designed for SemEval-2015, Task 10, Subtask B. The system employs a con- strained, supervised text categorization ap- proach. Firstly, since thorough preprocess- …...

We describe a classifier for predicting message-level sentiment of English micro- blog messages from Twitter. This paper describes our submission to the SemEval- 2015 competition (Task 10). Our approach is …...

This paper describes TwitterHawk, a system for sentiment analysis of tweets which partici- pated in the SemEval-2015 Task 10, Subtasks A through D. The system performed com- petitively, most notably …...

Recursive neural models, which use syn- tactic parse trees to recursively generate representations bottom-up, are a popular architecture. However there have not been rigorous evaluations showing for exactly which tasks …...

Sentiment classification aims to automati- cally predict sentiment polarity (e.g., pos- itive or negative) of user generated sen- timent data (e.g., reviews, blogs). Due to the mismatch among different domains, …...

Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. How- ever, one of the remaining challenges is to model long texts …...

We compare different word embeddings from a standard window based skipgram model, a skipgram model trained using dependency context features and a novel skipgram variant that utilizes additional information from …...

One may express favor (or disfavor) to- wards a target by using positive or neg- ative language. Here for the first time we present a dataset of tweets annotated for …...

We present our supervised sentiment classifi- cation system which competed in SemEval- 2016 Task 4: Sentiment Analysis in Twitter. Our system employs a Support Vector Ma- chine (SVM) classifier trained …...

AbstractWith the explosive growth of online social media (forums, blogs, and social networks), exploitation of these new information sources has become essential. Our work is based on the sud4science project. …...

AbstractSentiment classification becomes more and more important with the rapid growth of user generated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent …...

AbstractCollecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the …...

This paper describes our sentiment clas- sification system for microblog-sized doc- uments, and documents where a topic is present. The system consists of a soft- voting ensemble of a word2vec …...

AbstractThis paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex …...

AbstractThis paper proposes a matching technique for learning causal associations between word features and class labels in document classification. The goal is to identify more meaningful and generalizable features than …...

AbstractThis paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which …...

AbstractThis paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying …...

AbstractSentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. How to automatically construct sentiment lexicons has become a research topic in the field of …...

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, …...

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social …...

AbstractThere is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier …...

AbstractThis paper describes a system attended in the SemEval-2018 Task 1 “Affect in tweets” that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with …...

AbstractPooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention …...

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 …...

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 …...

AbstractK-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires …...

AbstractThe performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information …...

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 …...

AbstractNeural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to …...

AbstractStance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention …...

AbstractText attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models. The de facto standard method is to incorporate …...

AbstractPivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the …...

AbstractThe classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies …...

AbstractNLP models are shown to suffer from robustness issues, i.e., a model’s prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled …...

AbstractRecent neural network models have achieved impressive performance on sentiment classification in English as well as other languages. Their success heavily depends on the availability of a large amount of …...

AbstractWe study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak …...

AbstractMemes have become an ubiquitous social media entity and the processing and analysis of such multimodal data is currently an active area of research. This paper presents our work on …...

AbstractIn classification, there are usually some good features that are indicative of class labels. For example, in sentiment classification, words like good and nice are indicative of the positive sentiment …...

AbstractWord embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many …...

AbstractAttribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed …...

AbstractThe predictions of text classifiers are often driven by spurious correlations – e.g., the term “Spielberg” correlates with positively reviewed movies, even though the term itself does not semantically convey …...

AbstractIn this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic …...

AbstractWe propose hybrid models (HybridE and HybridW) for meme analysis (SemEval 2020 Task 8), which involves sentiment classification (Subtask A), humor classification (Subtask B), and scale of semantic classes (Subtask …...

AbstractWe present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning …...

AbstractPivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled …...

AbstractData programming aims to reduce the cost of curating training data by encoding domain knowledge as labeling functions over source data. As such it not only requires domain expertise but …...

AbstractWe propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics. The model incorporates contextualized representation with binary constituency parse tree to capture semantic composition. Comprehensive experiments demonstrate …...

AbstractSentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple …...

AbstractFor many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high. To address this problem, …...

Sentiment classification has been crucial for many natural language processing (NLP) applica- tions, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data …...

AbstractMemes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online …...

AbstractIn this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in …...

AbstractAspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, …...

AbstractRecently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of …...

AbstractThe flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have …...

AbstractConventional opinion polls were usually conducted via questionnaires or phone interviews, which are time-consuming and error-prone. With the advances in social networking platforms, it’s easier for the general public to …...

AbstractRomanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large …...

AbstractWe introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one …...

Automated predictions require explanations to be interpretable by humans. One type of ex- planation is a rationale, i.e., a selection of in- put features such as relevant text snippets from …...

AbstractData augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed …...

The ability to learn from limited data, or few- shot learning, is a desirable and often critical requirement for NLP systems. While many existing methods do poorly at learning from …...

Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since …...

AbstractRecently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). …...

AbstractMultilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen …...

AbstractPosting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due …...

AbstractRetrieval-augmented language models (LMs) use non-parametric memory to substantially outperform their non-retrieval counterparts on perplexity-based evaluations, but it is an open question whether they achieve similar gains in few- and …...

AbstractKnowledge distillation is an effective method to transfer knowledge from a large pre-trained teacher model to a compacted student model. However, in previous studies, the distilled student models are still …...

AbstractThis paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? …...

AbstractThis paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources …...

AbstractFor text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. …...

AbstractCross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this …...

AbstractThis paper describes a system built in the SemEval-2022 competition. As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks …...

AbstractParameter-Efficient Tuning (PET) has shown remarkable performance by fine-tuning only a small number of parameters of the pre-trained language models (PLMs) for the downstream tasks, while it is also possible …...

AbstractModels trained on real-world data tend to imitate and amplify social biases. Common methods to mitigate biases require prior information on the types of biases that should be mitigated (e.g., …...

AbstractDemographic biases and social stereotypes are common in pretrained language models (PLMs), and a burgeoning body of literature focuses on removing the unwanted stereotypical associations from PLMs. However, when fine-tuning …...

AbstractNLP models often degrade in performance when real world data distributions differ markedly from training data. However, existing dataset drift metrics in NLP have generally not considered specific dimensions of …...

AbstractLarge language models (LLMs) and generative AI have emerged as the most important areas in the field of natural language processing (NLP). LLMs are considered to be a key component …...

AbstractIn recent years, deep neural networks (DNNs) have achieved state-of-the-art performance on a wide range of tasks. However, limitations in interpretability have hindered their applications in the real world. This …...

AbstractThis paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the …...

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 …...

AbstractWe propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters – small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). …...

AbstractRecent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. Specifically, these methods utilize few-shot …...