  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.
