Pre-processing Online Financial Text for Sentiment Classification: A Natural Language Processing Approach

Sun Fan, Ammar Belatreche, SA Coleman, TM McGinnity, Yuhua Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages122-129
Number of pages8
Publication statusPublished - 27 Mar 2014
EventIEEE Computational Intelligence for Financial Engineering and Economics -
Duration: 27 Mar 2014 → …

Conference

ConferenceIEEE Computational Intelligence for Financial Engineering and Economics
Period27/03/14 → …

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  • Cite this

    Fan, S., Belatreche, A., Coleman, SA., McGinnity, TM., & Li, Y. (2014). Pre-processing Online Financial Text for Sentiment Classification: A Natural Language Processing Approach. In Unknown Host Publication (pp. 122-129). IEEE.