Sentence-level sentiment analysis via BERT and BiGRU

Jianghong Shen; Xiaodong Liao; Zhuang Tao

doi:10.1117/12.2550215

27 November 2019 Sentence-level sentiment analysis via BERT and BiGRU

Jianghong Shen, Xiaodong Liao, Zhuang Tao

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113212S (2019) https://doi.org/10.1117/12.2550215
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Jianghong Shen, Xiaodong Liao, and Zhuang Tao "Sentence-level sentiment analysis via BERT and BiGRU", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212S (27 November 2019); https://doi.org/10.1117/12.2550215

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