An approach to Tweets categorization by using machine learning classifiers in oil business

Aldahawi, Hanaa and Allen, Stuart Michael ORCID: https://orcid.org/0000-0003-1776-7489 2015. An approach to Tweets categorization by using machine learning classifiers in oil business. Presented at: CICLing 2015: 16th International Conference on Intelligent Text Processing and Computational Linguistics, Cairo, Egypt, 14-20 April 2015. Published in: Gelbukh, Alexander ed. Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II. Lecture Notes in Computer Science. , vol.9042 Springer, pp. 535-546. 10.1007/978-3-319-18117-2_40

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Abstract

The rapid growth in social media data has motivated the development of a real time framework to understand and extract the meaning of the data. Text categorization is a well-known method for understanding text. Text categorization can be applied in many forms, such as authorship detection and text mining by extracting useful information from documents to sort a set of documents automatically into predefined categories. Here, we propose a method for identifying those who posted the tweets into categories. The task is performed by extracting key features from tweets and subjecting them to a machine learning classifier. The research shows that this multi-classification task is very difficult, in particular the building of a domain-independent machine learning classifier. Our problem specifically concerned tweets about oil companies, most of which were noisy enough to affect the accuracy. The analytical technique used here provided structured and valuable information for oil companies.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319181165
ISSN:	0302-9743
Last Modified:	01 Nov 2022 09:21
URI:	https://orca.cardiff.ac.uk/id/eprint/87679

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