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    <abstract>Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.</abstract>
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%0 Conference Proceedings
%T SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
%A Tian, Hao
%A Gao, Can
%A Xiao, Xinyan
%A Liu, Hao
%A He, Bolei
%A Wu, Hua
%A Wang, Haifeng
%A Wu, Feng
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tian-etal-2020-skep
%X Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.
%R 10.18653/v1/2020.acl-main.374
%U https://aclanthology.org/2020.acl-main.374
%U https://doi.org/10.18653/v1/2020.acl-main.374
%P 4067-4076

Markdown (Informal)

[SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://aclanthology.org/2020.acl-main.374) (Tian et al., ACL 2020)

• SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (Tian et al., ACL 2020)

ACL

• Hao Tian, Can Gao, Xinyan Xiao, Hao Liu, Bolei He, Hua Wu, Haifeng Wang, and Feng Wu. 2020. SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4067–4076, Online. Association for Computational Linguistics.