Volume 2, Issue 4, July 2016, Page: 41-45
Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models
Kiplagat Wilfred Kiprono, School of computing and informatics, University of Nairobi, Nairobi, Kenya
Elisha Odira Abade, School of computing and informatics, University of Nairobi, Nairobi, Kenya
Received: Jun. 12, 2016;       Accepted: Jun. 23, 2016;       Published: Aug. 1, 2016
DOI: 10.11648/j.ijdst.20160204.11      View  4465      Downloads  158
Abstract
The transition from web 1.0 to web 2.0 has enabled direct interaction between users and its various resources and services such as social media networks. In this research paper we have analyzed algorithms for sentiment analysis which can be used to utilize this huge information. The goals of this paper is to device a way of obtaining social network opinions and extracting features from unstructured text and assign for each feature its associated sentiment in a clear and efficient way. In this project we have applied naïve bayes, support vector machines and maximum entropy for analysis and produced an analytical report of the three qualitatively and quantitatively. We performed the project empirically and analyzed the resulting data using an excel tool so as to obtain comparative analysis of the three algorithms for classification.
Keywords
Pos, Svm, Maxent, Naive Bayes, Feature Selection, Sentiment Classification, N-grams, Bigrams, Unigrams, Trigrams
To cite this article
Kiplagat Wilfred Kiprono, Elisha Odira Abade, Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models, International Journal on Data Science and Technology. Vol. 2, No. 4, 2016, pp. 41-45. doi: 10.11648/j.ijdst.20160204.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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