Abstract
Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.
Highlights
In the recent years, one of the most popular domain using sentiment analysis on Twitter is the stock market (Yan, Zhou, Zhao, Tian, & Yang, 2016)
We can observe that this study has achieved a high accuracy equals 90.38% for Hybrid Naïve Bayes Classifiers (HNBCs) with all classes
The five steps of the model presented in this study starts with data collection, filtration, determination of the polarity according to sentiments of people, classification by enhancing Naïve Bayes Classifiers (NBCs) and ends with the performance and evaluation stage
Summary
One of the most popular domain using sentiment analysis on Twitter is the stock market (Yan, Zhou, Zhao, Tian, & Yang, 2016). Twitter has more than 140 million active users who share almost more than 400 million tweets every day, sharing their thoughts with the public. They do talk about trending topics or share incidents and experiences to get views and feedbacks from other users. These tweets help users and organizations to collect valuable information in different domains (Atefeh & Khreich, 2015). Are taking a further step by using Twitter as a data source as it is conveniently available and accessible. Data from tweets cover different kinds of events, news, as well as economics data such as stock markets, financial data associated with market indicators, e-commerce and marketing statistics (Geser, 2011; Thompson, 2008)
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