基于多头自注意力池化与多粒度特征交互融合的微博情感分析

Microblog Sentiment Analysis with Multi-Head Self-Attention Pooling and Multi-Granularity Feature Interaction Fusion

【目的】高效、准确地挖掘微博文本中所蕴含的情感信息,提升情感分析效果。【方法】采用WoBERT Plus与ALBERT分别对词级文本与字级文本进行动态编码,接着利用卷积操作提取局部关键特征,然后利用跨通道特征融合与多头自注意力池化操作提取全局语义信息并筛选出关键数据,最后利用多粒度特征交互融合操作将字级与词级语义信息进行有效融合,利用Softmax函数输出分类结果。【结果】本文模型在weibo_senti_100k数据集上的准确率与F1值分别为98.51%、98.53%,在SMP2020-EWECT数据集上的准确率与F1值分别为80.11%、75.62%,其表现均优于各数据集上先进的情感分析模型。【局限】在进行情感分析时,未考虑视频、图片、语音等多模态信息。【结论】所提模型提升了微博文本情感分析的效果,可以有效地完成微博文本情感分析任务。

[Objective]This paper tries to efficiently and accurately extract sentiment information from Weibo texts and improve sentiment analysis performance.[Methods]First,we used WoBERT Plus and ALBERT to dynamically encode the character and word-level texts.Then,we extracted key local features with convolution operation.Next,we utilized cross-channel feature fusion and multi-head self-attention pooling operation to extract global semantic information and filter out critical data.Finally,we fused character-level and word-level semantic information using a multi-granularity feature interaction fusion operation and generated the classification results with the Softmax function.[Results]This model’s accuracy and F1 value were 98.51%and 98.53%on the weibo_senti_100k dataset and 80.11%and 75.62%on the SMP2020-EWECT dataset,respectively.Its performance was better than the advanced sentiment analysis models on each dataset.[Limitations]Our model does not include multimodal information such as video,image,and audio for sentiment classification.[Conclusions]The proposed model could effectively accomplish sentiment analysis of Weibo texts.