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Efficient and Parallel Framework for Analyzing the Sentiment

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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 515))

Abstract

With the advent of Web 2.0, user-generated content is led to an explosion of data on the Internet. Several platforms such as social networking, microblogging, and picture sharing exist that allow users to express their views on almost any topic. The user views express their emotions and sentiments on products, services, any action by governments, etc. Sentiment analysis allows quantifying popular mood on any product, service or an idea. Twitter is popular microblogging platform, which permits users to express their views in a very concise manner. In this paper, a new framework is crafted which carried out the entire chain of tasks starting with extraction of tweets to presenting the results in multiple formats using an ETL (Extract, Transform, and Load) big data tool called Talend. The framework includes a technique to quantify sentiment in a Twitter stream by normalizing the text and judge the polarity of textual data as positive, negative, or neutral. The technique addresses peculiarities of Twitter communication to enhance accuracy. The technique gives an accuracy of above 84% on standard datasets.

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Acknowledgements

This research was also supported by Tiger Analytics Pvt. Ltd. We are thankful to them for providing insight and expertise that greatly assisted our research.We are also thankful to Prachi Khokhar for her assistance in editing the research and her comments that greatly improved the manuscript.

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Authors and Affiliations

  1. International Institute of Information Technology, Bhubaneswar, Odisha, India

    Ankur Sharma & Gopal Krishna Nayak

Authors
  1. Ankur Sharma
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  2. Gopal Krishna Nayak
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Corresponding author

Correspondence to Ankur Sharma .

Editor information

Editors and Affiliations

  1. ANITS, Prof., Comp. Sci. & Engg. Dept. ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg Dept. of ECE, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. SCIS, University of Hyderabad , Hyderabad, India

    Siba K. Udgata

  4. KIIT University, School of Computer Engineering KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Sharma, A., Nayak, G.K. (2017). Efficient and Parallel Framework for Analyzing the Sentiment. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_14

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  • DOI: https://doi.org/10.1007/978-981-10-3153-3_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3152-6

  • Online ISBN: 978-981-10-3153-3

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