The Experts below are selected from a list of 28065 Experts worldwide ranked by ideXlab platform
Giuseppe Di Fabbrizio - One of the best experts on this subject based on the ideXlab platform.
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Emotion detection in email Customer Care
Computational Intelligence, 2013Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers’ email are critical in maximizing Customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall Customer Care experience, which in turn results in increased Customer retention.We describe amethod that uses salient features to identify emotional email in the Customer Care domain. Salient features in Customer Care related email are expressions of Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance
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emotion detection in email Customer Care
North American Chapter of the Association for Computational Linguistics, 2010Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers' emails are critical in maximizing Customer satisfaction. Such emails often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They provide valuable feedback to improve contact center processes and Customer Care, as well as, to enhance Customer retention. This paper describes a method for extracting salient features and identifying emotional emails in Customer Care. Salient features reflect Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action and/or report to authorities. Compared to a baseline system using word ngrams, our proposed approach with salient features resulted in a 20% absolute F-measure improvement.
Narendra Gupta - One of the best experts on this subject based on the ideXlab platform.
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Emotion detection in email Customer Care
Computational Intelligence, 2013Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers’ email are critical in maximizing Customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall Customer Care experience, which in turn results in increased Customer retention.We describe amethod that uses salient features to identify emotional email in the Customer Care domain. Salient features in Customer Care related email are expressions of Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance
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emotion detection in email Customer Care
North American Chapter of the Association for Computational Linguistics, 2010Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers' emails are critical in maximizing Customer satisfaction. Such emails often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They provide valuable feedback to improve contact center processes and Customer Care, as well as, to enhance Customer retention. This paper describes a method for extracting salient features and identifying emotional emails in Customer Care. Salient features reflect Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action and/or report to authorities. Compared to a baseline system using word ngrams, our proposed approach with salient features resulted in a 20% absolute F-measure improvement.
Mazin Gilbert - One of the best experts on this subject based on the ideXlab platform.
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Emotion detection in email Customer Care
Computational Intelligence, 2013Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers’ email are critical in maximizing Customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall Customer Care experience, which in turn results in increased Customer retention.We describe amethod that uses salient features to identify emotional email in the Customer Care domain. Salient features in Customer Care related email are expressions of Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance
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emotion detection in email Customer Care
North American Chapter of the Association for Computational Linguistics, 2010Co-Authors: Narendra Gupta, Mazin Gilbert, Giuseppe Di FabbrizioAbstract:Prompt and knowledgeable responses to Customers' emails are critical in maximizing Customer satisfaction. Such emails often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They provide valuable feedback to improve contact center processes and Customer Care, as well as, to enhance Customer retention. This paper describes a method for extracting salient features and identifying emotional emails in Customer Care. Salient features reflect Customer frustration, dissatisfaction with the business, and threats to either leave, take legal action and/or report to authorities. Compared to a baseline system using word ngrams, our proposed approach with salient features resulted in a 20% absolute F-measure improvement.
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mining Customer Care dialogs for daily news
IEEE Transactions on Speech and Audio Processing, 2005Co-Authors: Shona Douglas, Mazin Gilbert, Deepak Agarwal, T Alonso, Robert M Bell, D F Swayne, Christopher VolinskyAbstract:As large-scale deployments of spoken dialog systems in call centers become more common, a wealth of information is gathered about the call center business as well as the operation of these systems from their daily logs. This paper describes the "VoiceTone Daily News" data mining tool for analyzing this information and presenting it in a readily comprehensible and customizable form that is suitable for use by anyone from system designers to call center businesses. Relevant business and dialog features are extracted from the speech logs of caller-system interactions and tracked by trend analysis algorithms. We describe novel techniques for generating alerts on multiple data streams while avoiding redundant "knock-on" alerts. Some initial experiments with automated measures of dialog success are described as possible additional features to track. Features that move outside their expected bounds on a given day generate headlines as part of a website generated completely automatically from each day's logs. A "drill-down" facility allows headlines to be investigated all the way to viewing logs of individual interactions behind the headline and listening to the audio for individual turns.
David Konopnicki - One of the best experts on this subject based on the ideXlab platform.
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conversational document prediction to assist Customer Care agents
Empirical Methods in Natural Language Processing, 2020Co-Authors: Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka R Gunasekara, Yosi Mass, Sachindra Joshi, Luis A Lastras, David KonopnickiAbstract:A frequent pattern in Customer Care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents that Customer Care agents can use to facilitate users' needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.
Maria Spano - One of the best experts on this subject based on the ideXlab platform.
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a network based concept extraction for managing Customer requests in a social media Care context
International Journal of Information Management, 2020Co-Authors: Michelangelo Misuraca, Germana Scepi, Maria SpanoAbstract:Abstract Web 2.0 changed everyday life in many aspects, including the whole system that orbits around the purchase of products and services. This revolution necessarily involved also companies, because Customers became increasingly demanding. The diffusion of social media platforms pushed Customers to prefer this channel for quickly obtaining information and feedback about what they want to buy, as well as for asking help after the selling. In this framework, many organisations adopted a new way of providing assistance known as social Customer Care. A direct link to companies allows Customers to obtain real-time solutions. In this paper, we introduce a new strategy for automatically managing the information listed in the requests that Customers send to the social media accounts of companies. Our proposal relies on the use of network techniques for extracting high-level structures from texts, highlighting the different concepts expressed into the Customers’ written requests. The texts can be then organised on the basis of this new emerging information. An application to the requests sent to the AppleSupport service on Twitter shows the effectiveness of the strategy.
