Abstract—The increasing demands of e-commerce websites have caused a vast amount of opinions about Internet users’ products and services. Therefore, the aspect-based sentiment analysis (ABSA) has attracted much attention from academia and industry due to its application in many real-life problems in recent years. This problem is a complex task that aims to extract both the aspects and sentiments from the text input. Aspect Category Detection and Sentiment Polarity Classification are two of the challenging subtasks of ABSA, which detect a set of pre-defined categories and corresponding sentiment polarity for a given review. This paper presents an effective joint multi-task architecture based on neural network models to solve two tasks in the document-level ABSA datasets. Our model is designed to predict the whole mentioned aspect categories and corresponding sentiment polarities on the document-level datasets. We trained our model jointly on two tasks simultaneously and utilized the additional information of aspect category detection task for predicting the aspect categories and its sentiments for the specific domain. Our architecture can explore the hidden correlated information between categories and polarities in the review. Experiments on two Vietnamese language datasets in the restaurant domain and hotel domain demonstrated that our model outperforms the previous state of the art methods on two benchmark document-level dataset.
Index Terms—Aspect-based sentiment analysis, deep neural network, multi-task learning, Vietnamese corpora, document-level dataset.
Dang V. Thin, Lac S. Le, Hao M. Nguyen and Ngan L.T Nguyen are with the University of Information Technology, Vietnam National University Ho Chi Minh City, Vietnam (e-mail: {thindv, ngannlt}@uit.edu.vn, 17520669@gm.uit.edu.vn, haohaoit@gmail.com).
Cite: Dang Van Thin, Lac Si Le, Hao Minh Nguyen, and Ngan Luu-Thuy Nguyen, "A Joint Multi-task Architecture for Document-level Aspect-based Sentiment Analysis in Vietnamese," International Journal of Machine Learning and Computing vol. 12, no. 4, pp. 126-135, 2022.
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