mlsanityData to 14 Oct 2026

Entity-level Sentiment Analysis in Contact Center Telephone Conversations

arXiv 2210.13401 ↗First posted 24 Oct 2022cs.CLcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 24 Oct 2022.

Builds on

14 of 29 references.

References · 14

  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  4. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  5. 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
  6. 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 122
  7. 2020Position-Aware Tagging for Aspect Sentiment Triplet Extractioncited by 91
  8. 2016Aspect Level Sentiment Classification with Deep Memory Networkcited by 82
  9. 2019TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selectioncited by 74
  10. 2021Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarizationcited by 21
  11. 2022BLINK with Elasticsearch for Efficient Entity Linking in Business Conversationscited by 13
  12. 2021Improving Punctuation Restoration for Speech Transcripts via External Datacited by 8
2 more
  1. 2022An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcriptscited by 7
  2. 2020Attention Transfer Network for Aspect-level Sentiment Classificationcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.

4 papers.

  1. 2023AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coachingcited by 8
  2. 2022Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loopcited by 7
  3. 2025LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deploymentcited by 1
  4. 2025Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generationcited by 1