Language-Agnostic Model for Aspect-Based Sentiment Analysis

Md Shad Akhtar, Abhishek Kumar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya


Abstract
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
Anthology ID:
W19-0413
Volume:
Proceedings of the 13th International Conference on Computational Semantics - Long Papers
Month:
May
Year:
2019
Address:
Gothenburg, Sweden
Venues:
IWCS | WS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
154–164
Language:
URL:
https://www.aclweb.org/anthology/W19-0413
DOI:
10.18653/v1/W19-0413
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-0413.pdf