Sentiment analysis is a growing field at the intersection of linguistics and computer science
that attempts to automatically determine the sentiment contained in text. Sentiment can be …

Sentiment understanding has been a long-term goal of AI in the past decades. This paper
deals with sentence-level sentiment classification. Though a variety of neural network …

The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including
sociology, psychology, economics, linguistics, and computer science. A number of excellent …

Recommender systems aim at predicting users' preferences based on abundant information,
such as user ratings, demographics, and reviews. Although reviews are sparser than ratings …

Recognizing negative and speculative information is highly relevant for sentiment analysis.
This paper presents a machine‐learning approach to automatically detect this kind of …

Affective 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 …

Negation handling is an important sub-task in Sentiment Analysis. Negation plays a
significant role in written text. Negation terms in sentence often changes the polarity of entire …

Negation words, such as no and not, play a fundamental role in modifying sentiment of
textual expressions. We will refer to a negation word as the negator and the text span within …

Negation and uncertainty modeling are long-standing tasks in natural language processing.
Linguistic theory postulates that expressions of negation and uncertainty are semantically …

Affective tasks, including sentiment analysis, emotion classification, and sarcasm detection
have drawn a lot of attention in recent years due to a broad range of useful applications in …