Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning

Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classificati...

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Main Authors: SONG, Kaisong, BING, Lidong, GAO, Wei, LIN, Jun, ZHAO, Lujun, WANG, Jiancheng, SUN, Changlong, LIU, Xiaozhong, ZHANG, Qiong
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/4555
https://ink.library.smu.edu.sg/context/sis_research/article/5558/viewcontent/D19_1019.pdf
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spelling sg-smu-ink.sis_research-55582019-12-26T08:34:17Z Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning SONG, Kaisong BING, Lidong GAO, Wei LIN, Jun ZHAO, Lujun WANG, Jiancheng SUN, Changlong LIU, Xiaozhong ZHANG, Qiong Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service satisfaction polarity. After that, we propose a novel Context Clue Matching Mechanism (CCMM) to enhance the representations of all customer utterances with their matched context clues, i.e., sentiment and reasoning clues. We construct two CS dialogue datasets from a top E-commerce platform. Extensive experimental results are presented and contrasted against a few previous models to demonstrate the efficacy of our model. 2019-11-07T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4555 info:doi/10.18653/v1/D19-1019 https://ink.library.smu.edu.sg/context/sis_research/article/5558/viewcontent/D19_1019.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
SONG, Kaisong
BING, Lidong
GAO, Wei
LIN, Jun
ZHAO, Lujun
WANG, Jiancheng
SUN, Changlong
LIU, Xiaozhong
ZHANG, Qiong
Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
description Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service satisfaction polarity. After that, we propose a novel Context Clue Matching Mechanism (CCMM) to enhance the representations of all customer utterances with their matched context clues, i.e., sentiment and reasoning clues. We construct two CS dialogue datasets from a top E-commerce platform. Extensive experimental results are presented and contrasted against a few previous models to demonstrate the efficacy of our model.
format text
author SONG, Kaisong
BING, Lidong
GAO, Wei
LIN, Jun
ZHAO, Lujun
WANG, Jiancheng
SUN, Changlong
LIU, Xiaozhong
ZHANG, Qiong
author_facet SONG, Kaisong
BING, Lidong
GAO, Wei
LIN, Jun
ZHAO, Lujun
WANG, Jiancheng
SUN, Changlong
LIU, Xiaozhong
ZHANG, Qiong
author_sort SONG, Kaisong
title Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
title_short Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
title_full Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
title_fullStr Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
title_full_unstemmed Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
title_sort using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4555
https://ink.library.smu.edu.sg/context/sis_research/article/5558/viewcontent/D19_1019.pdf
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