Twitter Dataset

Summarizing and analyzing Twitter content is an important and challenging task. In this pa- per, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a …...

Disambiguation to Wikipedia (D2W) is the task of linking mentions of concepts in text to their corresponding Wikipedia entries. Most previous work has focused on linking terms in formal texts …...

Activities on social media increase at a dra- matic rate. When an external event happens, there is a surge in the degree of activities re- lated to the event. These …...

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 …...

AbstractTwo challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather …...

AbstractThis work investigates how automated methods can be used to classify social media text into argumentation types. In particular it is shown how supervised machine learning was used to annotate …...

AbstractWe study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In …...

AbstractNowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person’s opinion about a product …...

We examine communications in a social network to study user emotional contrast – the propensity of users to express dif- ferent emotions than those expressed by their neighbors. Our analysis …...

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. …...

This work presents our team solution for task 4a (Message Polarity Classification) at the Se- mEval 2016 challenge. Our experiments have been carried out over the Twitter dataset pro- vided …...

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 …...

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 …...

Human emotions are complex and nuanced. Yet, an overwhelming majority of the work in automatically detecting emotions from text has focused only on classifying text into positive, negative, and neutral …...

AbstractThe mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited …...

AbstractImages and text represent types of content which are used together for conveying user emotions in online social networks. These contents are usually associated with a sentiment category. In this …...

AbstractAs the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage …...

AbstractSarcasm is a subtle form of language in which people express the opposite of what is implied. Previous works of sarcasm detection focused on texts. However, more and more social …...

AbstractThe prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover …...

AbstractTask oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) …...

AbstractParaphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream …...

AbstractHuman natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional …...

AbstractThe task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as …...

AbstractRumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of …...

AbstractEmotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional …...

AbstractAspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion …...

AbstractMillions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a …...

AbstractCode-Mixing (CM) is a common phenomenon in multilingual societies. CM plays a significant role in technology and medical fields where terminologies in the native language are not available or known. …...

AbstractThe conceptualization of a claim lies at the core of argument mining. The segregation of claims is complex, owing to the divergence in textual syntax and context across different distributions. …...

AbstractThe stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the …...

AbstractSentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter …...

AbstractThis paper describes the DuluthNLP system that participated in Task 12 of SemEval-2023 on AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset. Given a set of tweets, the …...

AbstractAnnotators’ disagreement in linguistic data has been recently the focus of multiple initiatives aimed at raising awareness on issues related to ‘majority voting’ when aggregating diverging annotations. Disagreement can indeed …...