Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction

By Thien Hai Nguyen, Kiyoaki Shirai. 2015

230 citations recorded by the source corpus.

The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07% in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43% and 6.07%. The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weiwei Jiang
2. Yuntao Yao
3. Yumo Xu
4. Philip S. Yu
5. B. Fang
6. Senzhang Wang
7. Shay B. Cohen
8. Xi Zhang

In Management Science and Operations Research

1. Weiwei Jiang
2. Yuntao Yao
3. Yumo Xu
4. Philip S. Yu
5. B. Fang
6. Yunjia Zhang
7. Senzhang Wang
8. Shay B. Cohen

Citations in the corpus

• Improving stock market prediction via heterogeneous information fusion (2017, background): “One main data source is the news articles [10, 11, 23, 24, 25].”
• Social media big data analytics: A survey (2019, unknown): “Social media analytics involves the use of di ff erent modeling and analytical techniques from various fi elds (T. H. Nguyen & Shirai, 2015).”
• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…et al. (2018) LSTM ARIMA Tsang et al. (2018) LSTM WT+SAEs+LSTM (Bao et al., 2017) Xu & Cohen (2018) StockNet Random Guess, ARIMA, RF, TSLDA (Nguyen & Shirai, 2015), HAN (Hu et al., 2018b) Yan & Ouyang (2018) WT+LSTM MLP, SVM, kNN Continued on next page Table 6 – continued from previous…”
• Natural language based financial forecasting: a survey (2017, unknown): “Sentiment analysis resources, such as Opinion Lexicon [52], are proposed; topic model [8] is used to discover both aspect and the related sentiment [82].”
• Stock Movement Prediction from Tweets and Historical Prices (2018, methodology): “• TSLDA: a generative topic model jointly learning topics and sentiments (Nguyen and Shirai, 2015).”