Harvesting social media sentiment analysis to enhance stock market prediction using deep learning

PeerJ Computer Science

Pooja Mehta¹  Ketan Kotecha²  Sharnil Pandya² 

[1] Faculty of Technology & Engineering, C. U. Shah University, Wadhvan, Surendranagar, Gujarat, India;Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, Maharastra, India;

关键词: Stock prediction;    Sentiment analysis;    Machine learning;    Deep learning;    LSTM;   

DOI  :  10.7717/peerj-cs.476

来源: DOAJ

【 摘 要 】

Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.

【 授权许可】

Unknown