International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
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 Volume 5, Issue 4 (April 2018), Pages: 115-123

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 Original Research Paper

 Title: Empirical comparison of sentiment analysis techniques for social media

 Author(s): Maria Hameed 1, Faizan Tahir 2, *, M. Ali Shahzad 1

 Affiliation(s):

 1Department of Computer Science, University of Sargodha, Lahore, Pakistan
 2Department of Computer Science, Virtual University of Pakistan, Faisalabad, Pakistan

 https://doi.org/10.21833/ijaas.2018.04.015

 Full Text - PDF          XML

 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, KEEL) available in different repositories are used for comparison purpose. We presented an empirical analysis of six classifiers. The analysis results that Support Vector Machine performs much better as compared to other. Efforts are made to provide a conclusion about different algorithms on the basis of numerical and graphical metrics to conclude that which algorithm is optimal. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Sentiment analysis, Social media, UCI database, KEEL support vector machine

 Article History: Received 29 November 2017, Received in revised form 15 February 2018, Accepted 27 February 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.04.015

 Citation:

 Hameed M, Tahir F, M. and Shahzad A (2018). Empirical comparison of sentiment analysis techniques for social media. International Journal of Advanced and Applied Sciences, 5(4): 115-123

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I4/Hameed.html

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