Open AccessText Polarity Detection using Multiple Supervised Machine Learning Algorithms
Author(s) -
Soummya Kar,
Mousumi Saha,
Tamasree Biswas
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8449.019320
Subject(s) - clarity , sentiment analysis , computer science , polarity (international relations) , social media , public opinion , machine learning , scale (ratio) , artificial intelligence , data science , algorithm , statement (logic) , statistical classification , world wide web , political science , biochemistry , chemistry , genetics , physics , quantum mechanics , biology , politics , law , cell
Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..