Sentiment Analysis Techniques and Approaches
Saismita Panda, Saumya Gupta, Swati Kumari, Parul Yadav · Zenodo (CERN European Organization for Nuclear Research) · 2021
Sentiment analysis or opinion mining is the extraction and detailed examination of opinions and attitudes from any form of text. Sentiment analysis is a very useful method widely used to express the opinion of a large group or mass. This sentiment can be based on the attitude of the author or his/her affective state at the moment of writing the text. Social media and other online platforms contain a huge amount of unstructured data in the form of tweets, blogs, posts, etc.This paper aims at analyzing a solution for the sentiment classification at a fined grained level, namely the sentence level in which the polarity of the sentence can be given by the three categories as positive, negative or neutral. In this paper, we have analyzed the popular techniques adopted in the classical Sentiment Analysis problem of analyzing Movie reviews like Naïve Bayes, K-Nearest Neighbour, Random Forest, Maximum Entropy, SVM, and Voted Perceptrons discussed in various papers with their advantages and disadvantages in detail and how many times have they provided researchers with satisfying results.