A Deep Learning System for Sentiment Analysis of Service Calls

Yanan Jia


Abstract
Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and discourse. Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans and AI more intelligent. In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations - that is, service calls. Based on the acoustic and linguistic features extracted from the source information, a novel aggregated method for voice sentiment recognition framework is built. Each party’s sentiment pattern during the communication is investigated along with the interaction sentiment pattern between all parties.
Anthology ID:
2020.ecnlp-1.4
Volume:
Proceedings of The 3rd Workshop on e-Commerce and NLP
Month:
July
Year:
2020
Address:
Seattle, WA, USA
Venues:
ACL | ECNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–34
Language:
URL:
https://www.aclweb.org/anthology/2020.ecnlp-1.4
DOI:
10.18653/v1/2020.ecnlp-1.4
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecnlp-1.4.pdf
Video:
 http://slideslive.com/38931243