Open Access iconOpen Access

ARTICLE

crossmark

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Muhammad Rizwan Rashid Rana1,*, Saif Ur Rehman1, Asif Nawaz1, Tariq Ali1, Azhar Imran2, Abdulkareem Alzahrani3, Abdullah Almuhaimeed4,*

1 University Institute of Information Technology–PMAS Arid Agriculture University, Rawalpindi, 46000, Pakistan
2 Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan
3 Computer Engineering and Science Department, Faculty of Computer Science and Information Technology, Al Baha University, Al Baha, 65799, Saudi Arabia
4 Digital Health Institute, King Abdulaziz City for Science and Technology, Riyadh, 11442, Saudi Arabia

* Corresponding Authors: Muhammad Rizwan Rashid Rana. Email: email; Abdullah Almuhaimeed. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2415-2428. https://doi.org/10.32604/csse.2023.035149

Abstract

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Keywords


Cite This Article

APA Style
Rana, M.R.R., Rehman, S.U., Nawaz, A., Ali, T., Imran, A. et al. (2023). Aspect-based sentiment analysis for social multimedia: A hybrid computational framework. Computer Systems Science and Engineering, 46(2), 2415-2428. https://doi.org/10.32604/csse.2023.035149
Vancouver Style
Rana MRR, Rehman SU, Nawaz A, Ali T, Imran A, Alzahrani A, et al. Aspect-based sentiment analysis for social multimedia: A hybrid computational framework. Comput Syst Sci Eng. 2023;46(2):2415-2428 https://doi.org/10.32604/csse.2023.035149
IEEE Style
M.R.R. Rana et al., "Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework," Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 2415-2428. 2023. https://doi.org/10.32604/csse.2023.035149



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1452

    View

  • 511

    Download

  • 8

    Like

Share Link