IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, Asif Ekbal
Abstract
Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.- Anthology ID:
- D18-1377
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3402–3411
- Language:
- URL:
- https://aclanthology.org/D18-1377
- DOI:
- 10.18653/v1/D18-1377
- Cite (ACL):
- Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, and Asif Ekbal. 2018. IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3402–3411, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (Majumder et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1377.pdf
- Code
- senticnet/IARM
- Data
- SemEval-2014 Task-4