Developing resources for sentiment analysis of informal Arabic text in social media

ITANI, Maher, ROAST, Chris and AL-KHAYATT, Samir (2017). Developing resources for sentiment analysis of informal Arabic text in social media. Procedia Computer Science, 117, 129-136.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: 10.1016/j.procs.2017.10.101

Abstract

Natural Language Processing (NLP) applications such as text categorization, machine translation, sentiment analysis, etc., need annotated corpora and lexicons to check quality and performance. This paper describes the development of resources for sentiment analysis specifically for Arabic text in social media. A distinctive feature of the corpora and lexicons developed are that they are determined from informal Arabic that does not conform to grammatical or spelling standards. We refer to Arabic social media content of this sort as Dialectal Arabic (DA) - informal Arabic originating from and potentially mixing a range of different individual dialects. The paper describes the process adopted for developing corpora and sentiment lexicons for sentiment analysis within different social media and their resulting characteristics. The addition to providing useful NLP data sets for Dialectal Arabic the work also contributes to understanding the approach to developing corpora and lexicons.

Item Type: Article
Additional Information: Paper presented at the 3rd International Conference on Arabic Computational Linguistics, ACLing 2017, 5-6 November 2017, Dubai, United Arab Emirates
Uncontrolled Keywords: sentiment analysis corpora lexicons Arabic language social media
Research Institute, Centre or Group: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number: 10.1016/j.procs.2017.10.101
Related URLs:
Depositing User: Chris Roast
Date Deposited: 22 Nov 2017 17:31
Last Modified: 11 Dec 2017 05:52
URI: http://shura.shu.ac.uk/id/eprint/17206

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