Sentiments or opinions from social media provide the most up-to-date and inclusive
information, due to the proliferation of social media and the low barrier for posting the …

Sentiment analysis requires a lot of information coming from different sources and about
different topics to be retrieved and fused. For this reason, one of the most important subtasks …

Misinformation such as fake news is one of the big challenges of our society. Research on
automated fact-checking has proposed methods based on supervised learning, but these …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

This paper introduces how ClaimBuster, a fact-checking platform, uses natural language
processing and supervised learning to detect important factual claims in political discourses …

Sentiment analysis is the task of automatically determining from text the attitude, emotion, or
some other affectual state of the author. This chapter summarizes the diverse landscape of …

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions,
sentiments, evaluations, attitudes, and emotions from written language. It is one of the most …

Sentiment analysis or opinion mining is the computational study of people's opinions,
appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and …

The paper gives an overview of the different sentiment classification approaches and tools
used for sentiment analysis. Starting from this overview the paper provides a classification of …