EMNLP 2022industry14 citations
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.
BibTeX
@inproceedings{fu-etal-2022-entity,
title = "Entity-level Sentiment Analysis in Contact Center Telephone Conversations",
author = "Fu, Xue-yong and
Chen, Cheng and
Laskar, Md Tahmid Rahman and
Gardiner, Shayna and
Hiranandani, Pooja and
Tn, Shashi Bhushan",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.49/",
doi = "10.18653/v1/2022.emnlp-industry.49",
pages = "484--491"
}