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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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 |
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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 |
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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. |
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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 |
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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 |
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Institutional Knowledge at Singapore Management University |
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2019 |
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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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