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Sentiment Analysis of Twitter Data Using Clustering and Classification

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Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 421))

Abstract

Data mining helps in collecting and managing data besides performing analysis and prediction analysis. The process that is implemented to discover useful data patterns may have different names. Statisticians, database researchers, and professional organizations were among the first to use term data mining. The fundamental steps for sarcasm detection are dataset collection, feature extraction, and classification. This work puts forward a new model of sarcasm detection formed by fusing K-mean, PCA, and SVM classifiers together. With respect to common evaluation metrics like accuracy, precision, and recall, the architecture designed for this work is especially productive.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Burdwan, Burdwan, India

    Santanu Modak & Abhoy Chand Mondal

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  1. Santanu Modak
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  2. Abhoy Chand Mondal
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Corresponding author

Correspondence to Santanu Modak .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KIET Group of Institutions, Ghaziabad, Delhi, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Science and Engineering, Nirma University, Ahmedabad, India

    Sudeep Tanwar

  4. Federal University of Piauí, Teresina, Brazil

    Joel J. P. C. Rodrigues

  5. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

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Modak, S., Mondal, A.C. (2023). Sentiment Analysis of Twitter Data Using Clustering and Classification. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_51

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  • DOI: https://doi.org/10.1007/978-981-19-1142-2_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1141-5

  • Online ISBN: 978-981-19-1142-2

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