Joint Learning for Aspect and Polarity Classification in Persian Reviews Using Multi-Task Deep Learning

The purpose of this paper focuses on two sub-tasks related to aspect-based sentiment analysis, namely, aspect category detection (ACD) and aspect category polarity (ACP) in the Persian language. Its ability to identify all aspects discussed in the text is what makes aspect-based sentiment analysis so important and useful. While aspect-based sentiment analysis analyses all aspects of the text, it will be most useful when it is able to identify their polarity along with their identification. Most of the previous methods only focus on solving one of these sub-tasks separately or use two separate models. Thus, the process is pipelined, that is, the aspects are identified before the polarities are identified. In practice, these methods lead to model errors that are unsuitable for practical applications. In other words, ACD mistakes are sent to ACP. In this paper, we propose a multi-task learning model based on deep neural networks, which can concurrently detect aspect category and detect aspect category polarity. We evaluated the proposed method using a Persian language dataset in the movie domain on different deep learning-based models. Final experiments show that the CNN model has better results than other models. The reason is CNN's capability to extract local features. Since sentiment is expressed using specific words and phrases, CNN has been able to be more efficient in identifying these in this dataset.iments show that the CNN model has better results than other models.