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  4894      Downloads  179
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.
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 © 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.
Li Yung-Ming, Li Tsung-Ying. Deriving market intelligence from microblogs. Decis Support Syst 2013.
Caro Luigi Di, Grella Matteo. Sentiment analysis via dependency parsing. Comput Stand Interfaces 2012.
Liu B. Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 2012.
Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends Inform Retriev 2008; 2: 1–135.
Mohammad SM. From once upon a time to happily ever after: tracking emotions in mail and books. Decis Support Syst 2012 s; 53: 730–41.
Fully Automatic Lexicon Expansion for Domain- oriented Sentiment Analysis by Hiroshi Kanayama Tetsuya Nasukawa, Tokyo Research Laboratory, IBM Japan, Ltd. 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242- 8502 Japan {hkananasukawa}@jp.ibm.com
Text normalization in social media: progress, problems and applications for a pre-processing system of casual English - Eleanor Clarka* and Kenji Arakia Pre-processing very noisy text - Alexander Clark, ISSCO / TIM, University of Geneva, UNI-MAIL, Boulevard du Pont-d’Arve, CH-1211 Geneva 4, Switzerland.
M. Almashraee, D. M. Diaz, and R. Unland, “Sentiment classification of online products based on machine learning techniques and multi-agent systems technologies,” in Industrial Conference on Data Mining - Workshops, 2012.
Mugenda, M., Mugenda, G. (1999). Research Methods. Quantitative and Qualitative Approaches. Nairobi, Kenya.
Pauls, Adam, and Dan Klein. "Faster and smaller n-gram language models." Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies. Vol. 1. 2011.
Socher, Richard, et al. "Semi-supervised recursive auto encoders for predicting sentiment distributions." Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011.
Kennedy, Alistair, and Diana Inkpen. "Sentiment classification of movie reviews using contextual valence shifters." Computational Intelligence 22.2 (2006): 110-125.
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