关键词: 心理咨询, 心理健康情感词典, 艾宾浩斯遗忘曲线, 交互注意力机制, 双向长短期记忆网络

Abstract: Abstract: Sentiment classification in psychological counseling aims to obtain the sentiment polarity of the inquirer’s utterance, which can provide support for establishing psychological counseling 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, this paper proposes a model based on Attention Over Attention mechanism. According to the model, historical sentiment words are assigned weights by temporal sequence. Thus it improved the accuracy of sentiment classification in psychological counseling. In a dialogue, historical sentiment word sequences of both sides were extracted by constructing a sentiment lexicon of mental health. Subsequently, the current sentence and two sequences of historical sentiment word were input into the Bidirectional Long Short-Term Memory (BiLSTM) to get corresponding feature vectors. The Ebbinghaus forgetting curve was used to allocate internal weights for the sequences of historical sentiment word. Both inertia features and interaction features were captured by Attention Over Attention (AOA) mechanism. Then, the above two features were input into the classification layer among with the text features, the sentiment polarity was carried out at final. Experimental results on public dataset Emotional First Aid Dataset show that the model proposed in this paper improves 1.55% compared with Caps-DGCN model in F1. Hence it can effectively improve the sentiment classification performance of psychological counseling.

Key words: psychological counseling, sentiment lexicon of mental health, the Ebbinghaus forgetting curve, Attention Over Attention mechanism, Bidirectional Long Short-Term Memory