Corpus for Customer Purchase Behavior Prediction in Social Media

Shigeyuki Sakaki, Francine Chen, Mandy Korpusik, Yan-Ying Chen

Abstract
Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. “want a Xperia” as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58% of want user tweets and 35% of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products.
Anthology ID:
L16-1475
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2976–2980
Language:
URL:
https://aclanthology.org/L16-1475
DOI:
Bibkey:
Cite (ACL):
Shigeyuki Sakaki, Francine Chen, Mandy Korpusik, and Yan-Ying Chen. 2016. Corpus for Customer Purchase Behavior Prediction in Social Media. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2976–2980, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Corpus for Customer Purchase Behavior Prediction in Social Media (Sakaki et al., LREC 2016)
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https://preview.aclanthology.org/paclic-22-ingestion/L16-1475.pdf
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