Asian Research Thesis Index

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 Na?ve 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.

Add/Update Thesis