Stock market analysis using social networks

Man Li, Deepak Puthal, Chi Yang, Yun Luo, Jin Zhang, Jianxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Nowadays, the use of social media has reached unprecedented levels. Among all social media, with its popular micro-blogging service, Twitter enables users to share short messages in real time about events or express their own opinions. In this paper, we examine the effectiveness of various machine learning techniques on retrieved tweet corpus. A machine learning model is deployed to predict tweet sentiment, as well as gain an insight into the correlation between twitter sentiment and stock prices. Specifically, that correlation is acquired by mining tweets using Twitter's search API and process it for further analysis. To determine tweet sentiment, two types of machine learning techniques are adopted including Naïve Bayes classification and Support vector machines. By evaluating each model, we discover that support vector machine gives higher accuracy through cross validation. After predicting tweet sentiment, we mine historical stock data using Yahoo finance API, while the designed feature matrix for stock market prediction includes positive, negative, neutral and total sentiment score and stock price for each day. In order to capturing the correlation situation between tweet opinions and stock market prices, hence, evaluating the direct correlation between tweet sentiments and stock market prices, the same machine learning algorithm is implemented for conducting our empirical study.

Original languageBritish English
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference, ACSW 2018
ISBN (Electronic)9781450354363
StatePublished - 29 Jan 2018
Event2018 Australasian Computer Science Week Multiconference, ACSW 2018 - Brisbane, Australia
Duration: 29 Jan 20182 Feb 2018

Publication series

NameACM International Conference Proceeding Series


Conference2018 Australasian Computer Science Week Multiconference, ACSW 2018


  • Machine learning technique
  • Sentiment analysis
  • Stock prediction
  • Twitter opinions


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