Pre-processing online financial text for sentiment classification : A natural language processing approach

Fan, S; Belatreche, A; Coleman, SA; McGinnity, TM; Li, Y

Abstract

Online financial textual information contains a large amount of investor sentiment, i.e. subjective assessment and discussion with respect to financial instruments. An effective solution to automate the sentiment analysis of such large amounts of online financial texts would be extremely beneficial. This paper presents a natural language processing (NLP) based pre-processing approach both for noise removal from raw online financial texts and for organizing such texts into an enhanced format that is more usable for feature extraction. The proposed approach integrates six NLP processing steps, including a developed syntactic and semantic combined negation handling algorithm, to reduce noise in the online informal text. Three-class sentiment classification is also introduced in each system implementation. Experimental results show that the proposed pre-processing approach outperforms other pre-processing methods. The combined negation handling algorithm is also evaluated against three standard negation handling approaches.

Citation

Fan, S., Belatreche, A., Coleman, S., McGinnity, T., & Li, Y. (2014, March). Pre-processing online financial text for sentiment classification : A natural language processing approach. Presented at The Institute of Electrical and Electronics Engineers (IEEE) : Computational Intelligence for Financial Engineering and Economics Conference, London

• Presentation Conference Type: Other
• Conference Name: The Institute of Electrical and Electronics Engineers (IEEE) : Computational Intelligence for Financial Engineering and Economics Conference
• Conference Location: London
• Start Date: Mar 27, 2014
• End Date: Mar 28, 2014
• Publication Date: Mar 27, 2014
• Deposit Date: Jun 19, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Publisher URL: http://dx.doi.org/10.1109/CIFEr.2014.6924063
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6901616
• Additional Information: Event Type : Conference