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Comparative Analysis of Sentiment Analysis Techniques for Social Media

Thesis Info

Access Option

External Link

Author

Maria Hamid

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2017

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=118

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676720971079

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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.
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