股市预测是金融业最困难的事业之一,volatile,嘈杂,和非参数字符。然而,随着计算机科学的进步,智能模型可以帮助投资者和分析师最大限度地降低投资风险。社交媒体和其他在线门户网站上的舆论是股市预测的重要因素。COVID-19大流行刺激了在线活动,因为个人被迫呆在家里,带来大量的舆论和情绪。这项研究的重点是在COVID-19暴涨期间,使用长期短期记忆网络(LSTM)对具有公众情绪的股市走势进行预测。这里,七种不同的情绪分析工具,VADER,逻辑回归,Loughran-McDonald,亨利,TextBlob,线性SVC,还有斯坦福,用于对来自四个在线来源的网络抓取数据进行情绪分析:与股票相关的文章标题,tweets,财经新闻来自“经济时报”和Facebook的评论。利用所处理的28个意见度量中的每一个的感觉得分和真实股票信息进行预测。通过使用线性SVC从Facebook评论中计算情绪评级,准确率达到98.11%。此后,将七种工具中每一种的四个估计情绪分数与股票数据逐步整合,以确定对股票市场的总体影响。当所有四个情绪得分与股票数据配对时,七个工具中有五个的预测准确性是最值得注意的,线性SVC计算分数有助于股票数据达到98.32%的最高准确率。
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%.