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    <titleInfo>
        <title>A Deep Learning System for Sentiment Analysis of Service Calls</title>
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        <namePart type="given">Yanan</namePart>
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            <title>Proceedings of the 3rd Workshop on e-Commerce and NLP</title>
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        <name type="personal">
            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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        <name type="personal">
            <namePart type="given">Surya</namePart>
            <namePart type="family">Kallumadi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nicola</namePart>
            <namePart type="family">Ueffing</namePart>
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        <name type="personal">
            <namePart type="given">Oleg</namePart>
            <namePart type="family">Rokhlenko</namePart>
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            <namePart type="given">Eugene</namePart>
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            <namePart type="family">Guy</namePart>
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    <abstract>Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and discourse. Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans and AI more intelligent. In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations - that is, service calls. Based on the acoustic and linguistic features extracted from the source information, a novel aggregated method for voice sentiment recognition framework is built. Each party’s sentiment pattern during the communication is investigated along with the interaction sentiment pattern between all parties.</abstract>
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    <identifier type="doi">10.18653/v1/2020.ecnlp-1.4</identifier>
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        <date>2020-07</date>
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            <start>24</start>
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%0 Conference Proceedings
%T A Deep Learning System for Sentiment Analysis of Service Calls
%A Jia, Yanan
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 3rd Workshop on e-Commerce and NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA
%F jia-2020-deep
%X Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and discourse. Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans and AI more intelligent. In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations - that is, service calls. Based on the acoustic and linguistic features extracted from the source information, a novel aggregated method for voice sentiment recognition framework is built. Each party’s sentiment pattern during the communication is investigated along with the interaction sentiment pattern between all parties.
%R 10.18653/v1/2020.ecnlp-1.4
%U https://aclanthology.org/2020.ecnlp-1.4
%U https://doi.org/10.18653/v1/2020.ecnlp-1.4
%P 24-34

Markdown (Informal)

[A Deep Learning System for Sentiment Analysis of Service Calls](https://aclanthology.org/2020.ecnlp-1.4) (Jia, ECNLP 2020)

• A Deep Learning System for Sentiment Analysis of Service Calls (Jia, ECNLP 2020)

ACL

• Yanan Jia. 2020. A Deep Learning System for Sentiment Analysis of Service Calls. In Proceedings of the 3rd Workshop on e-Commerce and NLP, pages 24–34, Seattle, WA, USA. Association for Computational Linguistics.