A novel category detection of social media reviews in the restaurant industry

Khan, MU; Javed, AR; Ihsan, M; Tariq, U

Abstract

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches.

Citation

Khan, M., Javed, A., Ihsan, M., & Tariq, U. (2020). A novel category detection of social media reviews in the restaurant industry. Multimedia Systems, https://doi.org/10.1007/s00530-020-00704-2

• Journal Article Type: Article
• Acceptance Date: Oct 2, 2020
• Online Publication Date: Oct 24, 2020
• Publication Date: Oct 24, 2020
• Deposit Date: Dec 9, 2020
• Publicly Available Date: Oct 24, 2021
• Journal: Multimedia Systems
• Print ISSN: 0942-4962
• Electronic ISSN: 1432-1882
• Publisher: Springer Verlag
• DOI: https://doi.org/10.1007/s00530-020-00704-2
• Publisher URL: https://doi.org/10.1007/s00530-020-00704-2
• Related Public URLs: http://link.springer.com/journal/530
• Additional Information: Access Information : This is a post-peer-review, pre-copyedit version of an article published in Multimedia Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00530-020-00704-2