Abstract
Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.
Highlights
Sentiment analysis (SA) is one of the main tasks of natural language processing (NLP) that aims to extract opinions, thoughts, or attitudes toward a specific entity or subject
They show that the proposed model outperforms the baseline by more than 25% in F-1 score and achieves better results than related work models evaluated on the same dataset
The bidirectional gated recurrent unit (BiGRU)-based model outperforms more complicated models (e.g., C-IndyLSTM) by more than 1% in F-1 score using the same AraVec embeddings and more than 7% using contextualized embeddings
Summary
Sentiment analysis (SA) is one of the main tasks of natural language processing (NLP) that aims to extract opinions, thoughts, or attitudes toward a specific entity or subject. Online users are creating large amounts of data every day. Analyzing these unstructured datasets is valuable for many entities (e.g., governments, companies, and customers) and applications (recommender systems). SA can be handled on three levels, namely document level, sentence level, and aspect level. The first two levels identify the sentiment polarity of the whole document or sentence, which is not always beneficial. Users can express different opinions about different aspects or entities in the same text or review, making the aspect level, known as aspect-based sentiment analysis (ABSA), more suitable and practical for real-life scenarios
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