Latent Semantic Analysis Based Sentimental Analysis of Tweets in Social Media for the Classification of Cyberbullying Text

Authors

  • D. C. Joy Winnie Wise Professor, Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India.
  • S. Ambareesh Associate Professor, School of Computer Science and Engineering, Jain University, Bangalore, Karnataka, India.
  • Ramesh Babu P., Associate Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia.
  • D. Sugumar Associate Professor, Electronics and Communication Engineering, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, Tamil Nadu, India.
  • John Philip Bhimavarapu Assistant Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
  • A. Senthil Kumar Professor, Artificial Intelligence Department, Sri Vishnu Engineering College for Women, Bhimawaram, Andhra Pradesh, India.

Keywords:

Latent semantic analysis, sentimental analysis, tweets, cyberbullying text

Abstract

With wide spread of mobile technology, cyberbullying has developed as a substantial problem, particularly among adolescents. This is especially true in the case of adolescents. The fact that some people have chosen to end their own lives by committing suicide has also helped increase awareness of the issue among the broader population. Various methods are adopted to reduce the suicides and in broader sense, todays online media is highly prone to bullying that is termed as cyber bullying. Methods are adopted to detect the cyberbullying text, however most of them lacks clarity in detecting the accurate cyber bullying tweets. In this paper, Latent Semantic Analysis (LSA) based sentimental analysis of tweets in social media for the classification of cyberbullying text. The study uses LSA that helps in classifying the texts and help the user to post their opinions in social media without any online abuse. The simulation is conducted to test the efficacy of the classification model and the results show that the proposed method achieves higher rate of accuracy than other existing methods.

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References

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Published

05.12.2023

How to Cite

Wise, D. C. J. W. ., Ambareesh, S. ., Babu P., R. ., Sugumar, D. ., Bhimavarapu, J. P. ., & Kumar, A. S. . (2023). Latent Semantic Analysis Based Sentimental Analysis of Tweets in Social Media for the Classification of Cyberbullying Text. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 26–35. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4021

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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