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Sentiment Classification Using Hybrid Bayes Theorem Support Vector Machine Over Social Network

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Smart Innovations in Communication and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1168))

Abstract

Opinions or information can be shared on social media sites including, LinkedIn, blogs, Facebook, twitter, etc., in text form. Opinions or views about movies, products, politics or any interested topics of user can be shared using social networking sites in comments or feedback or picture form. Individuals opinion about political events, social, issues and products can be gathered and analysed by sentiment analysis. The proposed system includes preprocessing, feature extraction, sentiment classification using hybrid Bayes theorem support vector machine (HBSVM) algorithm. Preprocessing is used for removing unnecessary data, and it helps to improve the classification accuracy in the given dataset. Then, feature extraction is performed to select the prominent features based on the frequent terms. Then, apply HBSVM model for classifying neutral and non-neutral posts. Negative and positive are the class of non-neutral posts. Based on response to a post of various aspects, classification of group members is done. High performance is exhibited by proposed HBSVM as proven by results of experimentation when compared with existing techniques.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA, University, Mathura, 281406, India

    Shashi Shekhar & Narendra Mohan

Authors
  1. Shashi Shekhar
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  2. Narendra Mohan
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Corresponding author

Correspondence to Shashi Shekhar .

Editor information

Editors and Affiliations

  1. Computer Science Engineering Department, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Department of Computer Science and Engineering, National Institute of Technology Agartala, Agartala, Tripura, India

    Munesh C. Trivedi

  3. Computer Science Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India

    Krishn Kumar Mishra

  4. Computer Science Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, Uttar Pradesh, India

    A.K. Misra

  5. Department of Digital Technologies, University of Mauritius, Reduit, Moka, Mauritius

    Khedo Kavi Kumar

  6. Information Systems Department, Faculty of Information and Communication Technology, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia

    Erma Suryani

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Shekhar, S., Mohan, N. (2021). Sentiment Classification Using Hybrid Bayes Theorem Support Vector Machine Over Social Network. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K., Suryani, E. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-15-5345-5_10

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