"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Annotating Errors and Emotions in Human-Chatbot Interactions in Italian

This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall

Multi-task dialog act and sentiment recognition on Mastodon

International audienceBecause of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license

Deep Emotion Recognition in Textual Conversations: A Survey

While Emotion Recognition in Conversations (ERC) has seen a tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker and emotion dynamics modelling, to interpreting common sense expressions, informal language and sarcasm, addressing challenges of real time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC to interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities pertaining to this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions of the most prominent works in ERC with explanations of the Deep Learning architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. The survey highlights the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions and the benefits of incorporating annotation subjectivity in the learning phase

Tugging at the heartstrings? : examining discrete emotion in nonprofit Twitter messages and its effect on pass along behavior

The rise of social media has provided organizations with new tools for interacting with customers and building relationships and have created an ideal place to foster and nurture emotional connections. Nonprofit organizations now strongly rely on the sharing of their social media messages to deliver important information, build community, and mobilize supporters (Lovejoy & Saxton, 2012). However, research regarding the extent to which nonprofits use emotions in social media communications is quite limited. The inclusion of emotional content is important in message virality, however, only very limited research exists on the types of emotional content that is included in nonprofit Twitter messages. Therefore, relevant data and descriptive frameworks are essential to helping us understand how nonprofit organizations are using microblogging sites to engage with their target audiences. This research takes a first step in this regard to investigate the effect that emotion can have on pass along behavior. Using Social Sharing of Emotion (Rime Finkenauer, Luminet, Zech, and Philippot, 1998, Rime 2009) as the theoretical foundation, this dissertation specifically examines nonprofit usage of discrete emotion and its effect on pass along behavior. This research found that nonprofits are using emotional content in their Twitter messages to communicate with their public. Specifically, nonprofits are using the focal eight discrete emotions as follows: Trust (33.3%), anticipation (30.4), joy (27.9%), fear (17.2), surprise (13.8%), sadness (13.6%), anger (12.2%), and disgust (7.1%). Additionally, results indicate that using emotive content in nonprofit Twitter messages can influence pass along behavior. Specifically, results indicate that nonprofit messages that utilized fear, sadness, surprise, or trust positively influenced pass along behavior. In contrast, use of anticipation-related words had a negative impact of pass along behavior, and thus while it is currently the second most utilized emotion it should be used cautiously. Therefore, nonprofits can now better employ emotive content to extend the reach of the messages to see their messages spread further.Advertisin

Trust in the context of subscription contracts

Trust plays an essential role in interorganizational interactions. It reduces uncertainty, ensures long-term relationships, positively influences innovation, product adoption, and serves as a solution to the commitment problem. This work observes trust in the context of a Software as a Service (SaaS) market. In a case study of a SaaS service provider and their customers, I apply the Ability, Benevolence, Integrity trust framework to illustrate the effect of individual trust dimensions on the relationship between the customer and the service provider. First, for integrity-based trust, I show a positive effect of early interactions with customer success teams on product usage. Second, I show that benevolence-based trust increases customer engagement. Third, I use supervised machine learning and explainability methods to illustrate the positive effect of the ABI trust dimensions on customer contract extensions. Methodologically, this work suggests a strategy for machine learning applications in sociological research. Finally, this work derives practical managerial implications for service providers