Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions

• URL: http://arxiv.org/abs/2211.03923v1
• Date: Tue, 8 Nov 2022 00:43:36 GMT
• Title: Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions
• Authors: Juan Sebasti\'an Salcedo Gallo, Jes\'us Solano, Javier Hern\'an Garc\'ia, David Zarruk-Valencia, Alejandro Correa-Bahnsen
• Abstract summary: We propose a framework relying solely on chat-based customer support interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
• Score: 60.87845704495664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.

Related papers

• Robot Behavior Personalization from Sparse User Feedback [7.51373397483077]
  We create Task Adaptation using Abstract Concepts (TAACo) framework. TAACo can learn to predict the user's preferred manner of assistance with any given task. We show that TAACo outperforms GPT-4 by 16% and a rule-based system by 54%, on prediction accuracy.
  arXiv  Detail & Related papers  (2024-10-25T00:08:38Z)
• Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
  We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message. The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona.
  arXiv  Detail & Related papers  (2023-05-25T21:01:00Z)
• U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation [59.81301478480005]
  We construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledges.
  arXiv  Detail & Related papers  (2023-05-05T01:44:35Z)
• Sequence-aware item recommendations for multiply repeated user-item interactions [0.0]
  We design a recommender system that induces the temporal dimension in the task of item recommendation. It considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment.
  arXiv  Detail & Related papers  (2023-04-02T17:06:07Z)
• Linguistic Elements of Engaging Customer Service Discourse on Social Media [12.6970199179668]
  We analyze language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics. This paper demonstrates that we can predict engagement using initial customer and brand posts.
  arXiv  Detail & Related papers  (2022-12-24T18:49:03Z)
• Predictive analytics for appointment bookings [0.0]
  The first model predicts whether a customer will show up for the meeting, while the second model indicates whether a customer will book a premium service. Both models produce accurate results with more than a 75% accuracy rate. This paper offers a framework for resource planning using the predicted demand.
  arXiv  Detail & Related papers  (2022-04-18T14:02:15Z)
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy [83.61404191470126]
  We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors. The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
  arXiv  Detail & Related papers  (2022-04-07T14:11:31Z)
• Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management [61.88858330222619]
  We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
  arXiv  Detail & Related papers  (2021-11-11T19:40:51Z)
• Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots [51.091235903442715]
  This paper makes an attempt to explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection. Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways. Empirical studies on the Persona-Chat dataset show that the partner personas can improve the accuracy of response selection.
  arXiv  Detail & Related papers  (2021-05-19T10:32:30Z)
• End User Accounts of Dark Patterns as Felt Manipulation [27.30148897628138]
  We report on the results of a survey of users conducted in English and Mandarin Chinese. We identify both qualitatively-supported insights to describe end users' felt experiences of manipulative products, and a continuum of manipulation.
  arXiv  Detail & Related papers  (2020-10-21T14:55:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.