Sentiment Analysis of Review Data of a Product Using Python

  • Authors

    • P Santhi Priya
    • T Venkateswara Rao
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16452
  • Opinion mining, SPC, NLP, reviews, computations.

  • Abstract

    The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.

     

     

  • References

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      [2] Liu B (2010) Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, Second Edition. Taylor and Francis Group, Boca

      [3] Liu B, Hu M, Cheng J (2005) Opinion observer: Analyzing and comparing opinions on the web. In: Proceedings of the 14th IC on WWW ’05. ACM, New York, NY, USA. pp 342–351

      [4] Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh conference on International Language Resources and Evaluation. European Languages Resources Association, Valletta, Malta

      [5] Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based onminimum cuts. In: Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL ’04. Association for Computational Linguistics, Stroudsburg, PA, USA.

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  • How to Cite

    Santhi Priya, P., & Venkateswara Rao, T. (2018). Sentiment Analysis of Review Data of a Product Using Python. International Journal of Engineering & Technology, 7(3.12), 674-676. https://doi.org/10.14419/ijet.v7i3.12.16452

    Received date: 2018-07-28

    Accepted date: 2018-07-28

    Published date: 2018-07-20