Domain specific opinion mining

  • Authors

    • Surabhi Thorat
    • Dr. C.Namrata Mahender
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.21173
  • Social Media, Drought, Opinion Mining, Domain Specific, Polarity, N-Gram, Association Mining.

  • Abstract

    The manuscript Social media is a very promising platform of communication between the peoples. Remarkable work has been done re- cently focusing on the analysis of social media in order to analyze the people thinking and behavioral trends about current topics of inter- est but still many challenges are yet to be uncovered. In this paper, we focused on analyzing the domain specific tweets collected from social media. To improve the result accuracy firstly we had done the polarity test to find the polarity of tweets categorized in negative, positive and neural labels. Secondly we applied N-gram model that assigns probabilities to sentences and sequences of words started from unigram, bigram, and trigram up-to four gram. Lastly, we performed association mining on the tweets to find the association of do- main specific data with its back and forth paired text.

     

  • References

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      [2] Surabhi Thorat,†Opinion Mining and Sentiment Analysis- Its Tools and Challengesâ€, International Journal of IT, Engineering and Applied Sciences Research (IJIEASR) ISSN: 2319-4413 Volume 3, No. 11, November 2014, p. 12-15.

      [3] Lu Y., Castellanos M., Dayal U. and Zhai C. 2011.Automatic Con- struction of a Context-Aware Sentiment Lexicon: An Optimization Approach In Proceedings of the World Wide Web Confer- ence(WWW).

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      [6] N-grams,Speech and Language Processing. Daniel Jurafsky & James H. Martin. Draft of September 1, 2014.

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  • How to Cite

    Thorat, S., & C.Namrata Mahender, D. (2018). Domain specific opinion mining. International Journal of Engineering & Technology, 7(4.5), 628-630. https://doi.org/10.14419/ijet.v7i4.5.21173

    Received date: 2018-10-07

    Accepted date: 2018-10-07

    Published date: 2018-09-22