SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis

Jie Zhou, Junfeng Tian, Rui Wang, Yuanbin Wu, Wenming Xiao, Liang He


Abstract
Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users’ emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain. In this paper, we pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning. We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference. A series of experiments are conducted and the results indicate the great advantages of our model. We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SentiX can be trained with only 1% samples (18 samples) and it achieves better performance than BERT with 90% samples.
Anthology ID:
2020.coling-main.49
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
568–579
Language:
URL:
https://aclanthology.org/2020.coling-main.49
DOI:
10.18653/v1/2020.coling-main.49
Bibkey:
Cite (ACL):
Jie Zhou, Junfeng Tian, Rui Wang, Yuanbin Wu, Wenming Xiao, and Liang He. 2020. SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 568–579, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis (Zhou et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.49.pdf
Code
 12190143/sentix
Data
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