Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data

Heike Adel, Francine Chen, Yan-Ying Chen


Abstract
Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define the task of classifying the purchase stage of each tweet in a user’s tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.
Anthology ID:
E17-2094
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
592–598
Language:
URL:
https://aclanthology.org/E17-2094
DOI:
Bibkey:
Cite (ACL):
Heike Adel, Francine Chen, and Yan-Ying Chen. 2017. Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 592–598, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data (Adel et al., EACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/E17-2094.pdf