AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification
This work solves emotion classification problems for virtual chat bots in applications like social media and online support, but it appears incremental.
The paper tackles emotion recognition in conversations by proposing AdCOFE, which addresses issues like contextual information loss and token importance, resulting in improved performance on a standard dataset.
Emotion recognition in conversations is an important step in various virtual chat bots 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 and (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 the Emotion recognition in conversations dataset show that AdCOFE is beneficial in capturing emotions in conversations.