Domain specific opinion mining

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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.

     


  • Keywords


    Social Media; Drought; Opinion Mining; Domain Specific; Polarity; N-Gram; Association Mining.

  • References


      [1] G. Eason, Hulth, A. and Megyesi, B.B., “A Study on Automatically Extracted Keywords in Text Categorization”, Proceedings of the 21st International Conference on Computational Linguistics in 2006.

      [2] Surabhi Thorat,” Opinion Mining and Sentiment Analysis- Its Tools and Challenges”, 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).

      [4] Jijkoun V., de Rijke M., and Weerkamp W. 2010. Generating fo- cused topic-specific sentiment lexicons. In ACL '10, pages 585–594.

      [5] Choi Y. and Cardie C. 2009 adapting a polarity lexicon using inte- ger linear programming for domainspecific sentiment classification. In EMNLP '09, pages 590–598.

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


 

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Article ID: 21173
 
DOI: 10.14419/ijet.v7i4.5.21173




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