AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification

Emotion recognition in conversations is an important step in various virtual chatbots which require opinion-based feedback, like in social media threads, online support, and many more applications. Current emotion recognition in conversations models face issues like: (a) loss of contextual information in between two dialogues of a conversation, (b) failure to give appropriate importance to significant tokens in each utterance, (c) inability to pass on the emotional information from previous utterances. The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by performing unique feature extraction using knowledge graphs, sentiment lexicons and phrases of natural language at all levels (word and position embedding) of the utterances. Experiments on emotion recognition in conversations datasets show that AdCOFE is beneficial in capturing emotions in conversations.

Automated summary

Emotion recognition in conversations is crucial for various virtual chatbots, but current models face issues such as loss of contextual information and inability to pass on emotional information. The proposed AdCOFE model addresses these issues by utilizing knowledge graphs, sentiment lexicons, and natural language phrases for feature extraction, showing benefits in capturing emotions according to experiments on conversation datasets.