Split over-training for unsupervised purchase intention identification

Abd Yusof, Noor Fazilla and Lin, Chenghua and Han, Xiwu and Barawi, Mohamad Hardyman (2020) Split over-training for unsupervised purchase intention identification. International Journal of Advanced Trends in Computer Science and Engineering, 9 (3). pp. 3921-3928. ISSN 2278–3091

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Abstract

Recognizing user-expressed intentions in social media can be useful for many applications such as business intelligence, as intentions are intimately linked to potential actions or behaviors. This paper focuses on a binary classification problem: whether a text expresses purchase intention (PI) or not (non-PI). In contrast to existing research, which relies on labeled intention corpus or linguistic knowledge, we proposed an unsupervised method called split over-training for the PI identification task. Experiments on PI identification from tweets showed that our approach was effective and promising. The best classifying accuracy of 84.6% and PI F-measure of 70.4% was achieved, which are only 7.7% and 4.9% respectively lower than fully supervised models. This means our unsupervised method may provide reasonable preprocessing for intention corpus labeling or intention knowledge acquisition.

Item Type:	Article
Uncontrolled Keywords:	Intention analysis, Text analysis, Purchase intention identification
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	10 Mar 2021 16:06
Last Modified:	05 Jul 2023 16:02
URI:	http://eprints.utem.edu.my/id/eprint/24902
Statistic Details:	View Download Statistic

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