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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2021: Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices pp 396–412Cite as

Analysis of Sentimental Behaviour over Social Data Using Machine Learning Algorithms

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12798)

Abstract

A person’s sentiment is rigorously influenced by his emotional feelings which is evoked from every single incident, occurring every day in his surroundings. In this case, the decision that he makes is greatly affected by his sentiment rather than facts. Sentimental behavior can be applied to many applications in health, business, education, etc. This paper proposes and develops a sentimental behavior based on machine learning algorithms for pre-processing feature selection, and classification that helps identify, extract, quantify the feelings to twitter social dataset. Based on the Sentimental behavior, an analytical prediction has been developed that can be used to understand the behavior of the customers or users. Conducting the testing process for three state-of-the-art algorithms, the unique methodology is devised based on these proposed algorithms (support vector machine (SVM), logistic regression (LR), and XGboost), our extensive experiments show that our approach has high accuracy. Based on the testing results, the accurate sentimental behavior detection algorithm is identified and recommended to be used for textual data in the future.

Keywords

  • Machine learning
  • Opinion mining
  • Logistic regression
  • Support vector machine
  • Sentimental behavior detection

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Author information

Authors and Affiliations

  1. International IT University, Almaty, Kazakhstan

    Abdul Razaque

  2. Eastern Michigan University, Ypsilanti, MI, USA

    Fathi Amsaad & Dipal Halder

  3. Department of Computer Science, College of Charleston, Charleston, SC, USA

    Mohamed Baza

  4. The Higher Technical Center for Training and Production, Tripoli, Libya

    Abobakr Aboshgifa

  5. Computer Science and Engineering, The Sacred Heart University, West Campus East Building WCE*E-1137, Fairfield, USA

    Sajal Bhatia

Authors
  1. Abdul Razaque
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  2. Fathi Amsaad
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  3. Dipal Halder
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  4. Mohamed Baza
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  5. Abobakr Aboshgifa
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  6. Sajal Bhatia
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Corresponding author

Correspondence to Mohamed Baza .

Editor information

Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Prof. Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Prof. Dr. Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Dr. Moonis Ali

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Razaque, A., Amsaad, F., Halder, D., Baza, M., Aboshgifa, A., Bhatia, S. (2021). Analysis of Sentimental Behaviour over Social Data Using Machine Learning Algorithms. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_34

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  • DOI: https://doi.org/10.1007/978-3-030-79457-6_34

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  • Online ISBN: 978-3-030-79457-6

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