Deep Contextualized Embeddings for Sentiment Analysis of Arabic Book's Reviews

Abstract

Nowadays, sentiment analysis has become a vital research field involving various research fields, including data mining, computational linguistics, and natural language processing. It seeks to identify and extract the sentiment polarity of written textual data (post, tweet, review, etc.). Context-sensitive representations produced by the language model ELMo are generally aided with the polysemy and capturing words with diverse sentiment valence, allowing for more effective handling of the Arabic language's ambiguity and complexity. Recently, deep learning has shown robust data modelling capabilities for coping with complex and massive datasets. In various language models, it is considered the most advanced-based model. In this work, firstly, we investigate the effectiveness of deep learning models with context-sensitive representations. Finally, we develop a bidirectional LSTM deep model for sentiment analysis on Arabic book reviews with the ability to capture the polysemy in the context. This study aims to determine whether book reviews can be categorised as displaying positive or negative sentiments. The experiments prove that our proposed method considerably outperforms the state-of-the-art sentiment analysis tailored to Arabic book reviews.