Arabic Stock-News Sentiments and Economic Aspects using BERT Model

Abstract

Stock-market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies’ stocks. It supports making the right decision in investing or analysts’ evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiments to predict the polarity of Arabic stock news in microblogs based on Machine Learning and Deep Learning approaches. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained based on data collected from an official Saudi stock-market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy.