Journal of Data Analysis and Information Processing

Volume 8, Issue 4 (November 2020)

ISSN Print: 2327-7211   ISSN Online: 2327-7203

Google-based Impact Factor: 1.33  Citations  

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

HTML  XML Download Download as PDF (Size: 340KB)  PP. 309-319  
DOI: 10.4236/jdaip.2020.84018    83 Downloads   458 Views  

ABSTRACT

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.

Cite this paper

Kolasani, S. and Assaf, R. (2020) Predicting Stock Movement Using Sentiment Analysis of Twitter Feed with Neural Networks. Journal of Data Analysis and Information Processing, 8, 309-319. doi: 10.4236/jdaip.2020.84018.

Cited by

No relevant information.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.