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Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text

arXiv 2108.00400 ↗First posted 1 Aug 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

Chinese sentiment analysis (CSA) has always been one of the challenges in natural language processing due to its complexity and uncertainty. Transformer has succeeded in capturing semantic features, but it uses position encoding to capture sequence features, which has great shortcomings compared with the recurrent model. In this paper, we propose T-E-GRU for Chinese sentiment analysis, which combine transformer encoder and GRU. We conducted experiments on three Chinese comment datasets. In view of the confusion of punctuation marks in Chinese comment texts, we selectively retain some punctuation marks with sentence segmentation ability. The experimental results show that T-E-GRU outperforms classic recurrent model and recurrent model with attention.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 62 references that are in the map. First posted on arXiv 1 Aug 2021.

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12 more
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