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Proceedings Paper

Sentence-level sentiment analysis via BERT and BiGRU
Author(s): Jianghong Shen; Xiaodong Liao; Zhuang Tao
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Paper Abstract

Sentiment analysis is a significant task in nature language processing (NLP). Acquiring high quality word representations is a key point in the task. Specially we find that the same word has different meaning in different sentence, which should be recognized by computer. This idea cannot be done well by traditional way of word embeddings. In this paper, we propose a BERT(Bidirectional Encoder Representation from Transformers) + BiGRU (Bidirectional Gated Recurrent Unit) model which first put words into vector via BERT model, from which we can gain the contextualized embeddings, then perform the sentiment analysis by BiGRU. Experimental results prove that compared with various of different methods, our model has the best performing.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212S (27 November 2019); doi: 10.1117/12.2550215
Show Author Affiliations
Jianghong Shen, Fujian Normal Univ. (China)
Xiaodong Liao, Fujian Normal Univ. (China)
Zhuang Tao, Fujian Normal Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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