Literature Review on Feature Identification in Sentiment Analysis

Abstract

Now-a-days the volume of opinions about products, issues, events, and politics etc. on different social, e-commerce and review sites grows very rapidly. From both opinion holder and opinion target point of view, it is very difficult and time consuming task to analyze all the reviews from this massive amount of data on the web. So, there is a need of efficient method that automatically extracts the opinions and relevant features of the opinion target from the reviews and finally generates the feature wise summary. Sometimes people may use different words to express same feature, this may produce a misperception in the results during feature wise summary generation. To avoid this, we need to categorize similar features for precise classification of opinions based on these feature groups. Therefore, our study is targeting the most important tasks of feature based sentiment analysis that are feature extraction and feature categorization. This paper is about to cover the currently available techniques in these two areas. We have also focused on least addressed area in this domain giving an opportunity to researchers for future work. General TermsPattern Recognition

Domain Specific Lexicon Generation through Sentiment Analysis

“…Authors in [45] used a sentiment classification tool LDA (Latent Dirichlet Allocation) to measure the feelings of students about some specific subjects and topics by classifying documents according to the sentiment present in the chats of the students. The main goal of the work was to help the teachers to enhance the e-learning environment according to the moods of the learners.…”