The stock sonar - Sentiment analysis of stocks based on a hybrid approach

Ronen Feldman, Benjamin Rosenfeld, Roy Bar-Haim, Moshe Fresko

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

65 Scopus citations

Abstract

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 accuracy of an existing document-level sentiment classifier.

Original languageEnglish
Title of host publicationAAAI-11 / IAAI-11 - Proceedings of the 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference
Pages1642-1647
Number of pages6
StatePublished - 2011
Externally publishedYes
Event25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA, United States
Duration: 7 Aug 201111 Aug 2011

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Conference

Conference25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
Country/TerritoryUnited States
CitySan Francisco, CA
Period7/08/1111/08/11

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