Suggestion mining from online reviews using temporal convolutional network. (3rd October 2022)

• Record Type: Journal Article
• Authors: Rashidullah Khan, Usama Bin; Akhtar, Nadeem; Kidwai, Umar Tahir; Siddiqui, Ghufran Alam
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 25:Number 7(2022)
• Page Start: 2101
• Page End: 2110
• Publication Date: 2022-10-03
• Subjects: 68T50; Suggestion mining -- Online reviews -- Temporal convolutional network (TCN); Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2022.2133249
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: Business and brand owners are using social media networks to provide and deliver various services to their clients and collect information about their products from customers. Customers give their opinions as well as ideas for the improvement of the products on the review platforms and portals. Suggestion Mining is a technique of automatic extraction of these innovative ideas or suggestions from online source data. In this paper, we proposed TCN architecture for suggestion mining from online reviews. The TCN uses causal and dilated convolutional layers to process sequential or temporal data and captures long-term dependencies. TCN architecture on the dataset of SemEval-2019 subtask A is experimented. The dataset is highly imbalanced and to overcome this problem, the ensemble oversampling technique to balance the dataset is applied. TCN is also experimented with the attention mechanism. Our proposed model outperforms the existing works by achieving an F1 score of 82.0 %.