AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification

TL;DR

AdCOFE introduces a novel feature extraction method utilizing knowledge graphs and sentiment lexicons to improve emotion recognition in conversations, effectively capturing contextual and emotional nuances for virtual chat applications.

Contribution

It presents a new model that enhances emotion recognition by addressing contextual loss and token importance issues through advanced feature extraction techniques.

Findings

Improved accuracy in emotion classification on benchmark datasets

Effective preservation of contextual information across dialogue turns

Enhanced identification of significant tokens and emotional cues

Abstract

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.