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

Fan, Sun, Belatreche, Ammar, Coleman, SA, McGinnity, TM and Li, Yuhua (2014) Pre-processing Online Financial Text for Sentiment Classification: A Natural Language Processing Approach. In: IEEE Computational Intelligence for Financial Engineering and Economics. IEEE. 8 pp. [Conference contribution]

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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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:30250
Deposited By: Dr Sonya Coleman
Deposited On:24 Sep 2014 15:27
Last Modified:24 Sep 2014 15:27

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