Open AccessFeature Set Selection for Sentiment Analysis
Author(s) -
Ganesh K. Shinde
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38511
Subject(s) - sentiment analysis , support vector machine , computer science , feature selection , set (abstract data type) , purchasing , feature (linguistics) , artificial intelligence , subject (documents) , selection (genetic algorithm) , machine learning , data mining , world wide web , linguistics , marketing , business , philosophy , programming language
Abstract: With proliferation of online blogging web sites, hundreds of thousands of text posts are generated. Using this rich information facilitate educated purchasing of objects, discovering and public developments involving more than a few merchandise in the market, discovering political inclination of societies previous to a country wide election, and many others. Considering that the final decade, Sentiment evaluation has received increased attention from many researchers as a procedure for addressing subject matters, such as the a fore mentioned ones. This paper specializes in Sentiment evaluation and use of sentiment Features. In this paper we have created the feature set and given input to svm and result verified for sentiment. Keywords: Sentiment analysis, support vector machine, maximum entropy, with features, without features, artificial intelligence.