Open Access

ARTICLE

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

Muhammad Rizwan Rashid Rana^1,*, Saif Ur Rehman¹, Asif Nawaz¹, Tariq Ali¹, Azhar Imran², Abdulkareem Alzahrani³, Abdullah Almuhaimeed^4,*

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: ; Abdullah Almuhaimeed. Email:

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

Received 09 August 2022; Accepted 08 December 2022; Issue published 09 February 2023

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.

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Keywords

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