Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text,...

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Detalhes bibliográficos

Principais autores:	Shangyi Yan, Jingya Wang, Zhiqiang Song
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2022-07-01
coleção:	Future Internet
Assuntos:	dynamic character and word encoding multi-head self-attention pooling multi-granularity feature interactive fusion microblog sentiment analysis
Acesso em linha:	https://www.mdpi.com/1999-5903/14/8/234
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