We propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode�...

The results showed that DCCMM outperforms existing advanced sentiment analysis models and uses the multi-granularity feature interaction fusion operation to�...

Abstract: To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve�...

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the�...

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the�...

[Objective] This paper tries to efficiently and accurately extract sentiment information from Weibo texts and improve sentiment analysis performance.

Jun 7, 2023 � Microblog Sentiment Analysis with Multi-Head Self-Attention Pooling and Multi-Granularity Feature Interaction Fusion.

An encoder-decoder (Seq2Seq) model is used for sentiment analysis of text. By adding character-level embedding to the word embedding layer,�...

An encoder-decoder (Seq2Seq) model is used for sentiment analysis of text. By adding character-level embedding to the word embedding layer, the OOV problem of�...

Jul 4, 2024 � I am trying to implement a model for sentiment analysis in text data using self-attention. In this example, i am using multi-head attention but cannot be sure�...