Entity-level Sentiment Analysis in Contact Center Telephone Conversations
This work addresses the problem of understanding user emotions towards entities like products or companies in contact center conversations for business insights, but it appears incremental as it applies existing methods to a specific domain without novel breakthroughs.
The paper tackled entity-level sentiment analysis on English telephone conversation transcripts from contact centers, presenting two approaches: one using DistilBERT and another combining a convolutional neural network with heuristic rules, but it did not report concrete performance numbers or results.
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.