Pooja Hiranandani

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i-10 index

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Co-Authors

Shayna Gardiner

University of Toronto

Xue-Yong Fu

Simon Corston-Oliver

Microsoft (United States)

Elena Khasanova

University of Washington

Cheng Chen

Massachusetts General Hospital

Md Tahmid Rahman Laskar

York University

Cheng Chen

University of Science and Technology Beijing

Cheng Chen

Southern University of Science and Technology

Shashi Bhushan Tn

National College of Ireland

Shashi Bhushan TN

Cooperative Institutions

Southeast University

Southern University of Science and Technology

University of Electronic Science and Technology of China

University of Science and Technology Beijing

Guizhou University

State Key Laboratory of Millimeter Waves

China Aerodynamics Research and Development Center

Fuzhou University

Shandong University of Science and Technology

Eye & ENT Hospital of Fudan University

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Entity-level Sentiment Analysis in Contact Center Telephone Conversations

arXiv (Cornell University) (2022)

Xue-Yong Fu Cheng Chen Md Tahmid Rahman Laskar Shayna Gardiner Pooja Hiranandani

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.

Sentiment Analysis

10.48550/arxiv.2210.13401

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Developing a Production System for Purpose of Call Detection in Business Phone Conversations

Elena Khasanova Pooja Hiranandani Shayna Gardiner Cheng Chen Simon Corston-Oliver

Elena Khasanova, Pooja Hiranandani, Shayna Gardiner, Cheng Chen, Simon Corston-Oliver, Xue-Yong Fu. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track. 2022.

Chen

Mobile phone

Phone call

10.18653/v1/2022.naacl-industry.29

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Citations (2)

Entity-level Sentiment Analysis in Contact Center Telephone Conversations

Xue-Yong Fu Cheng Chen Md Tahmid Rahman Laskar Shayna Gardiner Pooja Hiranandani

Xue-yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shayna Gardiner, Pooja Hiranandani, Shashi Bhushan Tn. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2022.

Sentiment Analysis

Center (category theory)

10.18653/v1/2022.emnlp-industry.49

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Citations (5)

Developing a Production System for Purpose of Call Detection in Business Phone Conversations

arXiv (Cornell University) (2022)

Elena Khasanova Pooja Hiranandani Shayna Gardiner Cheng Chen Xue-Yong Fu

For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.

Call centre

Bootstrapping (finance)

Call management

10.48550/arxiv.2205.06904

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