A GCC Stock Market Classification Model using Sentiment Analysis based on HNBCs

Abstract

Sentiment analysis has become one of the most common method to classify stock market behaviour. Moreover, sentiment analysis has gained a lot of importance in the last decade especially due to the availability of data from social media such as Twitter. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. In this research, a model for GCC stock market classification based on sentiment analysis is constructed. It is designed to enhance the classification accuracy by the incorporation of tweet timestamp and location features, stock market domain expert labelling technique and the construction of a hybrid Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of six phases. Data collection, labelling technique, data pre-processing, classification, performance and evaluation, and the final phase is recognition for the stock market behaviour. The model produced a significant result in classifying stock market behaviour with accuracy more than 89%. The model is beneficial for investors and researchers. For investors, it enables them to formulate their plans based on accurate indicators whereby it reduces the risk in decision making. For researchers, it draws their attention to the importance of feature engineering, labelling technique, and the classifiers hybridization in enhancing the classification accuracy.