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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Abstract

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.

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