基于交互注意力机制的心理咨询文本情感分类模型

Sentiment classification model of psychological counseling text based on attention over attention mechanism

心理咨询场景下的情感分类旨在获得咨询者话语的情感倾向,为建立心理咨询AI助手提供支持。现有的方法利用语境信息获取文本情感倾向,但未考虑对话记录中当前句与前向近邻句之间的情感传递。针对这一问题,提出一种基于交互注意力(AOA)机制的心理咨询文本情感分类模型,根据时序对历史情感词分配权重,进而提高分类准确率。利用构建的心理健康情感词典分别提取对话双方的历史情感词序列,再将当前句和历史情感词序列输入到双向长短期记忆(BiLSTM)网络获取对应的特征向量,并利用艾宾浩斯遗忘曲线对历史情感词序列分配权重。通过AOA机制获得惯性特征和交互特征,并结合文本特征输入到分类层计算情感倾向概率。在公开数据集Emotional First Aid Dataset上的实验结果表明,相较于Caps-DGCN(Capsule network and Directional Graph Convolutional Network)模型,所提模型的F1值提高了1.55%。可见,所提模型可以有效提升心理咨询文本的情感分类效果。

Sentiment classification in psychological counseling scenes aims to obtain the sentiment polarity of the inquirer’s utterance,which can provide support for establishing psychological counseling Artificial Intelligence(AI)assistants.Existing methods obtain the sentiment polarity of text through contextual information,failing to consider the sentiment transmission between the current sentence and the forward neighbor sentences in the dialogue record.To address the issue,a model for sentiment classification of psychological counseling text was proposed based on Attention Over Attention(AOA)mechanism.Historical sentiment words were assigned weights by temporal sequence,which improved the accuracy of sentiment classification for psychological counseling text.In a dialogue,historical sentiment word sequences of both sides were extracted by constructed sentiment lexicon of mental health.Subsequently,the current sentence and two sequences of historical sentiment words were input into the Bidirectional Long Short-Term Memory(BiLSTM)network to get corresponding feature vectors.The Ebbinghaus forgetting curve was used to allocate internal weights to the sequences of historical sentiment words.Both inertia features and interaction features were captured by AOA mechanism.Then,the above two features along with the text features were input into the classification layer,calculating the probability of sentiment polarity.Experimental results on public dataset Emotional First Aid Dataset show that the proposed model improves F1 value by 1.55%compared with Capsule network and Directional Graph Convolutional Network(Caps-DGCN)model.Hence the proposed model can effectively improve the sentiment classification effect of psychological counseling text.