Phonetic-based microtext normalization for Twitter sentiment analysis

Satapathy, Ranjan, Guerreiro, Claudia, Chaturvedi, Iti, and Cambria, Erik (2017) Phonetic-based microtext normalization for Twitter sentiment analysis. In: Proceedings of the IEEE International Conference on Data Mining Workshops. pp. 407-413. From: ICDMW 2017: 17th IEEE International Conference on Data Mining Workshops, 18-21 November 2017, New Orleans, LA, USA.

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The proliferation of Web 2.0 technologies and the increasing use of computer-mediated communication resulted in a new form of written text, termed microtext. This poses new challenges to natural language processing tools which are usually designed for well-written text. This paper proposes a phonetic-based framework for normalizing microtext to plain English and, hence, improve the classification accuracy of sentiment analysis. Results demonstrated that there is a high (>0.8) similarity index between tweets normalized by our model and tweets normalized by human annotators in 85.31% of cases, and that there is an accuracy increase of >4% in terms of polarity detection after normalization.

Item ID: 63347
Item Type: Conference Item (Research - E1)
ISBN: 978-1-5386-3800-2
ISSN: 2375-9259
Keywords: Error correction, Microtext analysis, Sentiment analysis, Text normalization, Twitter
Copyright Information: © 2017 IEEE.
Date Deposited: 08 Jul 2020 05:12
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970117 Expanding Knowledge in Psychology and Cognitive Sciences @ 100%
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