We introduce DynaSent ('Dynamic Sentiment'), a new English-language benchmark task for
ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally …

There has been a good amount of progress in sentiment analysis over the past 10 years,
including the proposal of new methods and the creation of benchmark datasets. In some …

In the last few years thousands of scientific papers have investigated sentiment analysis,
several startups that measure opinions on real data have emerged and a number of …

Recently, ChatGPT has drawn great attention from both the research community and the
public. We are particularly interested in whether it can serve as a universal sentiment …

Sentiment analysis is used as a proxy to measure human emotion, where the objective is to
categorize text according to some predefined notion of sentiment. Sentiment analysis …

Neural methods for SA have led to quantitative improvements over previous approaches, but
these advances are not always accompanied with a thorough analysis of the qualitative …

Negation handling is a crucial aspect of sentiment analysis, as it presents challenges to
accurate sentiment classification by altering polarity and reducing reliability. Traditional …

Pre-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 …

Sentiment lexicons have been leveraged as a useful source of features for sentiment
analysis models, leading to the state-of-the-art accuracies. On the other hand, most existing …

Many existing attention-based deep learning approaches to sentiment analysis have
focused on words and represent an entire review text as a word sequence. However, these …