Predicting Stock Movement Using Sentiment Analysis of Twitter Feed with Neural Networks

• 作者: Rida Assaf Sai Vikram Kolasani
• 来源期刊: 数据分析和信息处理(英文)
• 学科: 经济
• 关键词: Tweets Sentiment Analysis with Machine Learning Support Vector Machines (SVM) Neural Networks Stock Prediction
• 年，卷（期）: 2020,（4）
• 页码范围: 309-319
• 页数: 11页
• 分类号: F42

摘要

External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this paper, we show the effectiveness of using Twitter posts to predict stock prices. We start by training various models on the Sentiment 140 Twitter data. We found that Support Vector Machines (SVM) performed best (0.83 accuracy) in the sentimental analysis, so we used it to predict the average sentiment of tweets for each day that the market was open. Next, we use the sentimental analysis of one year’s data of tweets that contain the “stock market”, “stocktwits”, “AAPL” keywords, with the goal of predicting the corresponding stock prices of Apple Inc. (AAPL) and the US’s Dow Jones Industrial Average (DJIA) index prices. Two models, Boosted Regression Trees and Multilayer Perceptron Neural Networks were used to predict the closing price difference of AAPL and DJIA prices. We show that neural networks perform substantially better than traditional models for stocks’ price prediction.