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.

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