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COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS TECHNIQUES FOR SOCIAL MEDIA

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Author: MARIA HAMID

Citable URI : https://vspace.vu.edu.pk/detail.aspx?id=118

Publisher : Virtual University

Date Issued: 4/23/2018 12:00:00 AM

Abstract

Nowadays the excessive use of internet produces a huge amount of data due to the social networks such as Twitter, Facebook, Orkut and Tumbler. These are microblogging sites and are used to share the people opinions and suggestions on daily basis relevant to the certain topic. These are beneficial for decision making or extracting conclusions. Analysis of these feeds aims to assess the thinking and comments of people about some personality or topic. Sentiment analysis is a type of text classification and is performed by various techniques such as Machine Learning Techniques and shows that the text is negative, positive or neutral. In this work, we provide a comparison of most recent sentiment analysis techniques such as Naïve Bayes, Bagging, Random Forest, Decision Tree, Support Vector Machine and Maximum entropy. The purpose of the study is to provide an empirical analysis of existing classification techniques for social media for analyzing the good performance and better information retrieval. A comprehensive comparative framework is designed to compare these techniques. Various benchmark datasets (UCI, KAGGLE) available in different repositories are used for comparison purpose. We presented an empirical analysis of six classifiers. The analysis results that Random Forest performs much better as compared to other. Efforts are made to provide a conclusion about different algorithms based on numerical and graphical metrics to conclude that which algorithm is optimal.


URI : https://vspace.vu.edu.pk/details.aspx?id=118

Citation: Hamid, M(2017), COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS TECHNIQUES FOR SOCIAL MEDIA. Virtual University of Pakistan, (Lahore, Pakistan).

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