Sentiment analysis on social media for stock movement prediction

•A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods were proposed to capture the topic-sentiment feature.•Integration of the sentiments was investigated via a large scale experiment.•Our model o...

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Bibliographic Details
Published in:Expert systems with applications Vol. 42; no. 24; pp. 9603 - 9611
Main Authors: Nguyen, Thien Hai, Shirai, Kiyoaki, Velcin, Julien
Format: Journal Article
Language:English
Published: Elsevier Ltd 30.12.2015
Elsevier
Subjects:
Accuracy
Artificial Intelligence
Classification
Computer Science
Construction
Data mining
Digital media
Document and Text Processing
Historic
Information Retrieval
Machine Learning
Mathematical models
Message board
Opinion mining
Prediction
Raw materials
Sentiment analysis
Social media
Social networks
Statistics
Stock
Web
Sentiment analysis
Message board
Social media
Classification
Prediction
Opinion mining
Stock
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A novel method for predicting stock price movement was presented.•Topics and sentiments of them were extracted from social media as the feature.•Two methods were proposed to capture the topic-sentiment feature.•Integration of the sentiments was investigated via a large scale experiment.•Our model outperformed other methods in the average accuracy of 18 stocks. The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07% better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83% better accuracy than historical price method, and 3.03% better than human sentiment method.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2015.07.052