Exploiting Topic based Twitter Sentiment for Stock Prediction

By Jianfeng Si, Arjun Mukherjee, B. Liu, Qing Li, Huayi Li, Xiaotie Deng. 2013

272 citations recorded by the source corpus.

How researchers see this paper

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

1. Weiwei Jiang
2. Yumo Xu
3. Erik Cambria
4. Frank Xing
5. Xi Zhang
6. Ting-Wei Liu
7. Shay B. Cohen
8. Xiao Ding

Citations in the corpus

• Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “Nguyen et al. [23] explicitly consider the topics relating to the target stocks, and extracting topics and related sentiments from social media to make prediction.”
• Improving stock market prediction via heterogeneous information fusion (2017, background): “Another widely used data source to extract sentiments is the social media [10, 30, 31].”
• Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, background): “…natural language processing, many methods have been proposed to explore additional information (mainly online text data) for stock forecasting, such as financial news (Xie et al., 2013; Ding et al., 2014), twitter sentiments (Si et al., 2013; Si et al., 2014), financial reports (Lee et al., 2014).”
• Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, background): “Apart from events, sentiment analysis is another perspective to the problem of stock prediction (Das and Chen, 2007; Tetlock, 2007; Tetlock et al., 2008; Bollen et al., 2011; Si et al., 2013).”
• Correcting Forecasts with Multifactor Neural Attention (2016, background): “, 2000), (Taylor & Buizza, 2003), and social media signals (Chen & Du, 2013), (Si et al., 2013), have been considered in literature for time series prediction applications before.”
• Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction (2015, methodology): “Continuous Dirichlet Process Mixture (cDPM) model was used to learn the daily topic set of Twitter messages to predict the stock market (Si et al., 2013).”
• Recognizing Depression from Twitter Activity (2015, background): “Many researchers are interested in analyzing such large-scale data, and prior analyses have used those for purposes such as estimating the future movement of stock market indices and predicting the results of elections [6, 23, 38, 39, 33].”
• Stock Movement Prediction from Tweets and Historical Prices (2018, methodology): “Instead of using only deterministic features, generative topic models were extended to jointly learn topics and sentiments for the task (Si et al., 2013; Nguyen and Shirai, 2015).”
• Natural language based financial forecasting: a survey (2017, methodology): “[124] Number of tuples occurrences Naı̈ve Bayes & Association Rules Experimentally tuned k-NN [59] Trend possibility distribution n-gram Language model Conditional probability maximization [40] Tf-idf weighted key words Support Vector Machine Split-and-merge segmentation [2] Text classification Regr”
• A survey of sentiment analysis in social media (2018, background): “Si et al. [112] utilized a continuous Dirichlet process mixture model to learn the daily topic set from Twitter to help predicting the stock market.”
• Deep Learning for Event-Driven Stock Prediction (2015, background): “Apart from events, sentiment is another perspective of deep semantic analysis of news documents [Das and Chen, 2007; Tetlock, 2007; Tetlock et al., 2008; Bollen et al., 2011; Si et al., 2013].”