Welcome to the InfoSci Platform
A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification

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

Rim Chiha, Mounir Ben Ayed, Célia da Costa Pereira
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 20
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781799860518|DOI: 10.4018/IJMDEM.2021100104
Cite Article Cite Article

MLA

Chiha, Rim, et al. "A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification." IJMDEM vol.12, no.4 2021: pp.52-71. http://doi.org/10.4018/IJMDEM.2021100104

APA

Chiha, R., Ben Ayed, M., & Pereira, C. D. (2021). A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification. International Journal of Multimedia Data Engineering and Management (IJMDEM), 12(4), 52-71. http://doi.org/10.4018/IJMDEM.2021100104

Chicago

Chiha, Rim, Mounir Ben Ayed, and Célia da Costa Pereira. "A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification," International Journal of Multimedia Data Engineering and Management (IJMDEM) 12, no.4: 52-71. http://doi.org/10.4018/IJMDEM.2021100104

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.