Split Over-Training for Unsupervised Purchase Intention Identification
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) o...
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| Main Authors: | , , , |
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| Format: | E-Article |
| Language: | English |
| Published: |
The World Academy of Research in Science and Engineering
2020
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/30325/1/Split%20Over-Training%20for%20Unsupervised%20Purchase%20Intention%20Identification_abstract.pdf http://ir.unimas.my/id/eprint/30325/ http://www.warse.org/ https://doi.org/10.30534/ijatcse/2020/214932020 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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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