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

Khan, MU, Javed, AR ORCID: https://orcid.org/0000-0002-0570-1813, Ihsan, M ORCID: https://orcid.org/0000-0002-8753-7884 and Tariq, U 2020, 'A novel category detection of social media reviews in the restaurant industry' , Multimedia Systems .

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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


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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Multimedia Systems
Publisher: Springer
ISSN: 0942-4962
Related URLs:
Depositing User: Dr Mansoor Ihsan
Date Deposited: 09 Dec 2020 08:40
Last Modified: 15 Feb 2022 17:14
URI: https://usir.salford.ac.uk/id/eprint/59043

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