Effective Attention Modeling for Aspect-Level Sentiment Classification

Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier


Abstract
Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.
Anthology ID:
C18-1096
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1121–1131
Language:
URL:
https://aclanthology.org/C18-1096
DOI:
Bibkey:
Cite (ACL):
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Effective Attention Modeling for Aspect-Level Sentiment Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1121–1131, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Effective Attention Modeling for Aspect-Level Sentiment Classification (He et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1096.pdf
Data
SemEval-2014 Task-4