Entity-level Sentiment Analysis in Contact Center Telephone Conversations

Xue-yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, Shashi Bhushan Tn


Abstract
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 neural network supplemented with some heuristic rules.
Anthology ID:
2022.emnlp-industry.49
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
484–491
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.49
DOI:
Bibkey:
Cite (ACL):
Xue-yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, and Shashi Bhushan Tn. 2022. Entity-level Sentiment Analysis in Contact Center Telephone Conversations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 484–491, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Entity-level Sentiment Analysis in Contact Center Telephone Conversations (Fu et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-industry.49.pdf