International Transaction Journal of Engineering,
Management, & Applied Sciences & Technologies


:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

ISSN 2228-9860
eISSN 1906-9642


Vol.11(13) (2020)


    Preethi Nanjundan (Department of Data Science, Christ (Deemed to be University), Lavasa, Pune, INDIA),
    K. Maheswari (Department of Computer Applications, School of Computing, Kalasalingam Academy Research and Education, Tamil Nadu, INDIA),
    Jayabrabu Ramakrishnan (Department of Information Technology, College of Computer Science and Information Technology, Jazan University, SAUDI ARABIA),
    Dinesh Mavalur (Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, SAUDI ARABIA),
    Azath Mubarakali (Department of CNE, College of Computer Science, King Khalid University, SAUDI ARABIA),
    S. Ramkumar (School of Computing, Kalasalingam Academy of Research and Education, Tamil Nadu, INDIA ).

    Disciplinary: Computer and Information Technology.

    ➤ FullText

    DOI: 10.14456/ITJEMAST.2020.250

    Keywords: Sentiment analysis; Machine learning; SVM linear grid; Twitter dataset; SVM model; SVM radial grid; Kappa value; SVM classifier; Negative sentiment; Neutral sentiment; Positive sentiment.

    Sentiment analysis uses supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. The twitter data set is analyzed using a support vector machine (SVM) classifier with various parameters. The content of the tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individuals. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve productivity. The performance of SVM radial kernel, SVM linear grid and SVM Radial Grid was compared and found that SVM linear grid performs better than other SVM models.

    Paper ID: 11A13D

    Cite this article:

    Nanjundan, P., Maheswari, K., Ramakrishnan, J., Mavalur, D., Mubarakali, A., Ramkumar, S. (2020). PERFORMANCE OF BIG DATA ANALYSIS OF SENTIMENTS IN TWITTER DATASET USING SVM MODELS. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 11(13), 11A13D, 1-13.


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