Research on Multi classification of Live Streaming Bullet Screen Emotions Based on Deep Learning
In order to improve the accuracy and efficiency of barrage analysis in live streaming scenarios,this paper proposes a multi classification model for barrage emotions,MacBERT-BIL-STM-CNN,which combines MacBERT pre trained language model and BILSTM-CNN model.Emotions are classified into seven emotional dimensions:joy,good,anger,sorrow,shock,evil,and fear;At the same time,considering the influence of the inherent information contained in e-motional symbols such as facial expressions and emoticons on bullet screen sentiment analysis,the replacement of facial expressions and emoticons was carried out.After comparative experi-ments,the evaluation metrics of the MacBERT-BILSTM-CNN model have been improved to varying degrees compared to CNN,BILSTM-CNN,and MacBERT models on the same dataset,indicating that the model has better performance in bullet emotion multi classification tasks;Compared with the original dataset,there is a certain improvement in the evaluation indicators after replacing emotional symbols,which proves that fully considering the intrinsic information contained in emotional symbols can improve the accuracy of barrage emotion tendency judg-ment.
BarrageMulti classification of emotionsPre trained language modelYan scriptEmoticons
NETL
NSTL
万方数据
