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

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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Bibliographic Details
Main Authors: Shangyi Yan, Jingya Wang, Zhiqiang Song
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Future Internet
Subjects:
dynamic character and word encoding
multi-head self-attention pooling
multi-granularity feature interactive fusion
microblog sentiment analysis
Online Access:https://www.mdpi.com/1999-5903/14/8/234

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https://www.mdpi.com/1999-5903/14/8/234

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