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TwitSenti: A Real-Time Twitter Sentiment Analysis and Visualization Framework

Author

Listed:
  • Jamuna S. Murthy

    (PES University, India)

  • G. M. Siddesh

    (Ramaiah Institute of Technology, India)

  • K. G. Srinivasa

    (National Institute of Technical Teachers Training and Research, Chandigarh, India)

Abstract

Twitter is considered as one of the world’s largest social networking sites which allow users to customize their public profile, connect with others and interact with connected users. The proposed work introduces a distributed real-time twitter sentiment analysis and visualization framework by implementing novel algorithms for twitter sentiment analysis called Emotion-Polarity-SentiWordNet. The framework is applied to build an interactive web application called “TwitSenti” which can benefit companies and other organizations in knowing the people’s sentiment towards the aspects such as brands, current events, etc., which in turn helps in quick decision-making and planning marketing strategies. The algorithm is validated against three existing classifiers and hence proved that Emotion-Polarity-SentiWordNet provides highest accuracy value of 85%. Also, the framework showed best scalability results when evaluated through web app as four node clusters, proves to be fast and can scale well with massive data.

Suggested Citation

  • Jamuna S. Murthy & G. M. Siddesh & K. G. Srinivasa, 2019. "TwitSenti: A Real-Time Twitter Sentiment Analysis and Visualization Framework," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 1-26, June.
    • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:02:n:s0219649219500138
    DOI: 10.1142/S0219649219500138
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    Cited by:

    1. Akilandeswari J. & Jothi G. & Dhanasekaran K. & Kousalya K. & Sathiyamoorthi V., 2022. "Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-27, January.

    More about this item

    Keywords

    Sentiment analysis; Twitter; Apache Storm; D3.js; Kafka; spout; bolt;
    All these keywords.

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