Mehta Pooja, Pandya Sharnil, Kotecha Ketan
Faculty of Technology & Engineering, C. U. Shah University, Wadhvan, Surendranagar, Gujarat, India.
Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, Maharastra, India.
PeerJ Comput Sci. 2021 Apr 13;7:e476. doi: 10.7717/peerj-cs.476. eCollection 2021.
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.
信息收集已成为评估人们行为和行动的一个不可或缺的部分。互联网被用作一个在线学习平台，用于分享和交流想法。人们可以通过流行的社交网站和个人博客，积极地对各种产品和服务给出评价和建议。包括推特、脸书和谷歌+在内的社交网站，都是用于分享观点的网站的例子。股票市场是经济的一个重要领域，在贸易和工业发展中发挥着重要作用。预测股票市场走势是研究人员熟知且感兴趣的领域。社交网络完美地反映了公众对时事的看法。财经新闻报道被认为会对股票趋势价格的回报产生影响，并且许多数据挖掘技术被用于处理股票市场的波动。机器学习可以提供一种更准确、更强大的方法来处理与股票市场相关的预测。我们试图确定一家公司的股价变动与公众对该公司表达的意见（情绪）之间的关联。我们设计并实现了一个除其他参数外还考虑公众情绪的股价预测准确性工具。所提出的算法考虑公众情绪、意见、新闻和历史股价来预测未来股价。我们使用包括支持向量机、朴素贝叶斯分类器、线性回归、朴素贝叶斯和长短期记忆在内的机器学习和深度学习方法进行了实验。我们的结果验证了所提出方法的成功。
