A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification

Rim Chiha, Mounir Ben Ayed and Célia da Costa Pereira
Additional contact information
Rim Chiha: REGIM-Lab, University of Sfax, Tunisia
Mounir Ben Ayed: REGIM-Lab, University of Sfax, Tunisia
Célia da Costa Pereira: I3S, University of Côte d'Azur, France

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2021, vol. 12, issue 4, 52-71

Abstract: The aspect-based sentiment analysis (ABSA) task consists of two closely related subtasks: aspect extraction and sentiment classification. However, the majority of previous studies looked into each task separately, limiting their effectiveness. In contrast, the integration of aspect extraction and sentiment classification into a single model improves results. The main focus in this work is to manage these two tasks into a new collapsed model. The proposed model relies upon the bidirectional long short-term memory (Bi-LSTM) architecture. On the one hand, it combines a multi-channel convolution layer with an optimization method for handling the aspect extraction task. On the other hand, it includes an attention mechanism based on the residual block and aspect position information for predicting the appropriate opinion orientation of an aspect. The experimental results demonstrate that the model achieved the best performance.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJMDEM.2021100104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jmdem0:v:12:y:2021:i:4:p:52-71

Access Statistics for this article

International Journal of Multimedia Data Engineering and Management (IJMDEM) is currently edited by Chengcui Zhang

More articles in International Journal of Multimedia Data Engineering and Management (IJMDEM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().