Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning

Telecom

Christina Nousi¹  Christos Tjortjis¹  Paraskevas Koukaras¹ 

[1] School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece;

关键词: sentiment analysis;    stock market prediction;    machine learning;    data mining;    classification;    microblogging;   

DOI  :  10.3390/telecom3020019

来源: DOAJ

【 摘 要 】

The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.

【 授权许可】

Unknown