Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews

TL;DR

This paper presents a joint deep learning approach for aspect category detection and polarity classification in Persian reviews, demonstrating effective multi-label classification with CNN and GRU models on a new dataset.

Contribution

It introduces a combined model for ACD and ACP in Persian sentiment analysis and compares four deep models, highlighting CNN and GRU as most effective.

Findings

CNN and GRU outperform LSTM and Bi-LSTM

High accuracy in Persian review sentiment analysis

Effective multi-label classification approach

Abstract

Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to specify which aspect of the entity comes up in opinion while ACP aims to specify the polarity of each aspect category from the ACD task. The previous works mostly propose separate solutions for these two sub-tasks. This paper focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The proposed method carries out multi-label classification where four different deep models were employed and comparatively evaluated to examine their performance. A dataset of Persian reviews was collected from CinemaTicket website including 2200 samples from 14 categories. The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics. The results indicate the high applicability and preference of the CNN and GRU models in comparison to LSTM and Bi-LSTM.