一种基于情感计算与层次化多头注意力机制的负面新闻识别方法

A Negative News Recognition Method Based on Emotional Computing and Hierarchical Multi-head Attention Mechanism

网络负面新闻识别在网络舆情监测中具有重要的研究意义.针对当前海量数据下负面新闻难以检测的问题,提出了一种基于情感计算与层次化多头注意力机制相结合的负面新闻识别方法.首先,从新闻文本中采用TF-IDF(Term Frequeney-Inverse Document Frquency)和语义相似度算法构建负面新闻情感词库;其次,采用情感倾向计算方法计算负面新闻情感词的情感倾向度;最后,将词语和词语的情感倾向度进行向量化表示,并采用层次化多头注意力机制进行正负面新闻的判定.情感计算和多头注意力机制的引入,对于捕获文本中的情感词语提供了很大帮助.最终本文基于真实的网络新闻文本数据与现有的多种算法进行对比,证明了该模型具有较好的识别效果,相比于Han模型和LSTM模型分别提高了0.67%和3.29%.

Identification of network negative news has important research significance in network public opinion monitoring.Aiming at the problem that negative news is difficult to detect under the current mass data,this paper proposes a method of negative news recognition based on emotional computing and hierarchical multi-head attention mechanism.Firstly,this paper uses TFIDF(Term Frequeney-Inverse Document Frquency)and emotional similarity algorithm to construct negative news emotional lexicon from news texts.Secondly,this paper uses the method of emotional tendency calculation to calculate the degree of emotional tendency of negative news affective words.Finally,the model vectorizes the emotional tendencies of words and expressions,and use hierarchical multi-attention model to judge the positive and negative emotions of news.The introduction of emotional computing and multi-attention mechanism is of great help in capturing emotional words in texts.Finally,this paper compares the real network news text data with many existing algorithms,and proves that the model has a good recognition effect.Compared with the Han model and LSTM model,it is increased by 0.67%and 3.29%respectively.