A novel category detection of social media reviews in the restaurant industry
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SPECIAL ISSUE PAPER
A novel category detection of social media reviews in the restaurant industry Mohib Ullah Khan1 · Abdul Rehman Javed2 · Mansoor Ihsan3 · Usman Tariq4 Received: 21 June 2020 / Accepted: 2 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Social media analytic · IIoT · LSTM · CNN · Deep learning · Text classification · Word embedding (Word2Vec) · Attention mechanism · Opinion mining
1 Introduction
* Abdul Rehman Javed [email protected] Mohib Ullah Khan [email protected] Mansoor Ihsan [email protected] Usman Tariq [email protected] 1
National University of Computer and Emerging Sciences, Islamabad, Pakistan
2
Department of Cyber Security, Air University, Islamabad, Pakistan
3
The University of Salford, Manchester, UK
4
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al‑Kharj, Saudi Arabia
The industrial internet of things (IIoT) is the use of smart interconnected sensors and instruments to enhance manufacturing and industrial processes. IIoT or industry 4.0 is characterized as machines, PCs, and people empowering intelligent modern tasks utilizing advanced data analytics for transformational business results [20]. A combination of social media with IIoT has been progressively seen in industrial applications, for example, the manufacturing industry, education industry, smart devices industry, restaurant industry, automobile industry, marketing, and retail industry, etc. [20]. IIoT uses the intensity of sensors, actuators, and constant examination
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