Das Nabanita, Sadhukhan Bikash, Chatterjee Tanusree, Chakrabarti Satyajit
Department of Computer Science & Engineering, Techno International New Town, Kolkata, West Bengal India.
University of Engineering and Management, Kolkata, West Bengal India.
Soc Netw Anal Min. 2022;12(1):92. doi: 10.1007/s13278-022-00919-3. Epub 2022 Jul 27.
Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran-McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.
由于股票市场具有复杂、多变、嘈杂和非参数的特点，预测股票市场是金融行业最困难的任务之一。然而，随着计算机科学的发展，智能模型可以帮助投资者和分析师将投资风险降至最低。社交媒体和其他在线平台上的公众舆论是股票市场预测的一个重要因素。新冠疫情促使人们居家，从而刺激了在线活动，产生了大量的公众舆论和情绪。本研究聚焦于在新冠疫情爆发期间，利用长短期记忆网络（LSTM），根据公众情绪来预测股票市场走势。在这里，七种不同的情感分析工具，即VADER、逻辑回归、洛夫兰-麦克唐纳、亨利、TextBlob、线性支持向量分类器（Linear SVC）和斯坦福情感分析工具，被用于对从四个在线来源抓取的网页数据进行情感分析：与股票相关的文章标题、推文、《经济时报》的财经新闻以及脸书评论。对于处理的28种舆论指标中的每一个，都利用情感得分和真实股票信息进行预测。通过使用线性支持向量分类器从脸书评论中计算情感评级，准确率达到了98.11%。此后，将七种工具中每种工具得出的四个估计情感得分与股票数据逐步整合，以确定对股票市场的总体影响。当所有四个情感得分与股票数据配对时，七种工具中有五种的预测准确率达到最高，其中线性支持向量分类器计算得出的得分帮助股票数据达到了98.32%的最高准确率。
