The Stock Sonar - Sentiment Analysis of Stocks Based on a Hybrid Approach

By Ronen Feldman, Benjamin Rozenfeld, Roy Bar-Haim, Moshe Fresko. 2011

101 citations recorded by the source corpus.

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in-text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accu- racy of an existing document-level sentiment classifier.

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. Arjun Mukherjee
2. Philip S. Yu
3. B. Liu
4. Ting-Wei Liu
5. Bing Liu
6. Xiao Ding
7. Yuntao Yao
8. Xiaotie Deng

In Management Science and Operations Research

1. Xiao Ding
2. Junwen Duan
3. Ting-Wei Liu
4. Yuntao Yao
5. Philip S. Yu
6. Xiaotie Deng
7. B. Liu
8. Jacob Zahavi

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].”
• Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, background): “Second, Feldman et al. (2011) can obtain only limited categories of events, and hence the scalability of their work is not strong.”
• Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news (2014, background): “[13,14,21] to assign a sentiment score to each news item in our dataset.”
• Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, background): “Feldman et al. (2011) introduced a hybrid approach for stock sentiment analysis based on companies’ news articles.”