智能人机对话中的负面情感检测

Negative Sentiment Detection in Intelligent Human-Machine Dialogue

人工智能使得智能人机对话系统的应用日趋广泛,但对话系统中常常含有低俗、暴力、谣言 等负面情感信息,给对话系统造成了不良的影响,因此检测对话系统中的负面情感显得至关重要.针 对智能人机对话系统中的负面情感内容,提出了一种基于深度学习的负面情感检测模型 ,该模型利 用预训练词向量和BiLSTM可以有效地捕捉文本语义与上下文信息.相对于传统的词典匹配算法, 大大减少了对词典的依赖程度,能够智能地识别相似的负面情感表达形式,可以更加有效地检测对 话系统中的负面情感,在净化对话系统中起到了重要作用.

Artificial intelligence makes the application of intelligent human-machine dialogue system more and more widely,but the dialogue system often contains negative sentiment information such as vulgarity,violence and rumors,which has a bad influence on the dialogue system.Therefore,it is very important to detect negative sentiment in the dialogue system.Aiming at the negative emotion content in the intelligent human-machine dialogue system,a negative sentiment detection model based on deep learning is proposed.This model can effectively capture text semantics and context information by using pre-training word vector and BiLSTM.Compared with the traditional dictionary matching algorithm,it greatly reduces the dependence on the dictionary,and also can intelligently identify similar negative sentiment expression forms,which makes detecting negative sentiment in the dialogue system more effectively and plays an important role in purifying the dialogue system.