| Peer-Reviewed

Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models

Received: 12 June 2016     Accepted: 23 June 2016     Published: 1 August 2016
Views:       Downloads:
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.

Published in International Journal on Data Science and Technology (Volume 2, Issue 4)
DOI 10.11648/j.ijdst.20160204.11
Page(s) 41-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Pos, Svm, Maxent, Naive Bayes, Feature Selection, Sentiment Classification, N-grams, Bigrams, Unigrams, Trigrams

References
[1] Li Yung-Ming, Li Tsung-Ying. Deriving market intelligence from microblogs. Decis Support Syst 2013.
[2] Caro Luigi Di, Grella Matteo. Sentiment analysis via dependency parsing. Comput Stand Interfaces 2012.
[3] Liu B. Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 2012.
[4] Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends Inform Retriev 2008; 2: 1–135.
[5] 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.
[6] 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
[7] 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.
[8] 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.
[9] Mugenda, M., Mugenda, G. (1999). Research Methods. Quantitative and Qualitative Approaches. Nairobi, Kenya.
[10] 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.
[11] 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.
[12] Kennedy, Alistair, and Diana Inkpen. "Sentiment classification of movie reviews using contextual valence shifters." Computational Intelligence 22.2 (2006): 110-125.
Cite This Article
  • APA Style

    Kiplagat Wilfred Kiprono, Elisha Odira Abade. (2016). Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. International Journal on Data Science and Technology, 2(4), 41-45. https://doi.org/10.11648/j.ijdst.20160204.11

    Copy | Download

    ACS Style

    Kiplagat Wilfred Kiprono; Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int. J. Data Sci. Technol. 2016, 2(4), 41-45. doi: 10.11648/j.ijdst.20160204.11

    Copy | Download

    AMA Style

    Kiplagat Wilfred Kiprono, Elisha Odira Abade. Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models. Int J Data Sci Technol. 2016;2(4):41-45. doi: 10.11648/j.ijdst.20160204.11

    Copy | Download

  • @article{10.11648/j.ijdst.20160204.11,
      author = {Kiplagat Wilfred Kiprono and Elisha Odira Abade},
      title = {Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models},
      journal = {International Journal on Data Science and Technology},
      volume = {2},
      number = {4},
      pages = {41-45},
      doi = {10.11648/j.ijdst.20160204.11},
      url = {https://doi.org/10.11648/j.ijdst.20160204.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20160204.11},
      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.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Comparative Twitter Sentiment Analysis Based on Linear and Probabilistic Models
    AU  - Kiplagat Wilfred Kiprono
    AU  - Elisha Odira Abade
    Y1  - 2016/08/01
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ijdst.20160204.11
    DO  - 10.11648/j.ijdst.20160204.11
    T2  - International Journal on Data Science and Technology
    JF  - International Journal on Data Science and Technology
    JO  - International Journal on Data Science and Technology
    SP  - 41
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2472-2235
    UR  - https://doi.org/10.11648/j.ijdst.20160204.11
    AB  - 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.
    VL  - 2
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • School of computing and informatics, University of Nairobi, Nairobi, Kenya

  • School of computing and informatics, University of Nairobi, Nairobi, Kenya

  • Sections