The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Acoustic and Lexical Sentiment Analysis for Customer Service Calls
Abstract
We describe the development of a sentiment analysis system for customer service calls, starting with the data acquisition and labeling, and proceeding to the algorithmic information extraction and modeling process from both spoken words and their acoustic expression. The proposed system is based on the combination of multiple acoustic and lexical models in a late fusion approach. Acoustic aspects of sentiment are captured by utterance-level features based on aggregated openSMILE and raw cepstral features, and further augmented with an energy contour model. Lexical aspects are captured by back-off n-gram language models. These models are found to combine effectively, showing different strengths as pertains to positive and negative sentiment detection.
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Cites
Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
Wagner, J., Andre, E., Lingenfelser, F., & Kim, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.12 · Cited by 156 · cited 1×
“Two of the approaches regarding the fusion of multiple knowledge sources are early and late fusion [5, 6, 7].”
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Flagged first, then by citations
Research works printed but not linked: 18
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SAFER: Situation Aware Facial Emotion Recognition
Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.09372 · Cited by 2
“Additionally, ER has a wide range of applications in domains such as human-computer interactions [1], law enforcement and surveillance [2], interactive gaming, consumer behavior analysis, customer service [3], and health care [4], among others.”
Background
Metadata
- Authors
- Li, B., Dimitriadis, D., & Stolcke, A.
- Affiliations
- Columbia University (US)
- Microsoft (United States) (US)
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 5876-5880
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2936162287
- Cited by
- 62
- References
- 1
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 14 · 2023: 10 · 2022: 9 · 2021: 6 · 2020: 6 · 2019: 3
- FWCI
- 2.30
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- sentiment analysis, customer service calls, acoustic features, lexical features, decision fusion, openSMILE, cepstral features, utterance-level features
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