Sentiment Analysis : A Literature Survey

TL;DR

This survey reviews the evolution, challenges, and diverse computational approaches in Sentiment Analysis, highlighting supervised techniques and cognitive psychology insights for understanding opinions in vast social data.

Contribution

It provides a comprehensive overview of various methods, challenges, and applications in Sentiment Analysis, including recent cognitive psychology perspectives.

Findings

Supervised techniques like SVM and Naive Bayes are effective but have limitations.

Cognitive psychology offers new insights into subjectivity and discourse structure.

Sentiment Analysis is crucial for mining opinions from social media and web data.

Abstract

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Na\"ive Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also study some specific topics in Sentiment Analysis and the contemporary works in those areas.