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With a view to inform the design of a mentor-like synthetic agent that is to engage in a coherent and consistent in character conversation with human subjects, we conducted a data-driven analysis of verbal communication between fictional mentor and mentee characters in films. While in our earlier work the focus was on the conversation strategies of mentor characters, here we present the extended model, wherein conversation activity of both mentor and mentee characters is accounted for. To examine and to formalize local communication actions and extended goals that the two characters achieve jointly, categories of intents, projects and relationship phases were introduced. The resulting annotated corpus of mentor and mentee characters' utterances was analyzed qualitatively and quantitatively. In furtherance of the automated in-character dialogue generation task, a range of the state-of-the-art approaches to automated utterances classification was evaluated. [ABSTRACT FROM AUTHOR]

Conversational robots and agents are being designed for educational and/or persuasive tasks, e.g., health or fitness coaching. To pursue such tasks over a long time, they will need a complex model of the strategic goal, a variety of strategies to implement it in interaction, and the capability of strategic talk. Strategic talk is incipient ongoing conversation in which at least one participant has the objective of changing the other participant’s attitudes or goals. The paper is based on the observation that strategic talk can stretch over considerable periods of time and a number of conversational segments. Film dialogues are taken as a source to develop a model of the strategic talk of mentor characters. A corpus of film mentor utterances is annotated on the basis of the model, and the data are interpreted to arrive at insights into mentor behavior, especially into the realization and sequencing of strategies. [ABSTRACT FROM PUBLISHER]

We analyze emotionally annotated massive data from Internet relay chat (IRC) as well as from BBC forum website and model the dialogues between chat participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution. This process is claimed to be responsible for a power-law distribution of the discussion lengths observed in the dialogues. We perform numerical simulations based on the noticed phenomenon obtaining a good agreement with the real data. Finally, we propose a method to artificially prolong the duration of the discussion that relies on the entropy of emotional probability distribution. [ABSTRACT FROM AUTHOR]

This paper presents an integrated view on a series of experiments conducted with an affective dialog system, applied as a tool in studies of emotions and social processes in online communication. The different realizations of the system are evaluated in three experimental setups to verify effects of affective profiles, as well as of fine-grained communication scenarios on users' expressions of affective states, experienced emotional changes, and interaction patterns. Results demonstrate that the system applied in virtual reality settings matches a Wizard-of-Oz in terms of chatting enjoyment, dialog coherence, and realism. Variants of the system's affective profile significantly influence the rating of chatting enjoyment and an emotional connection. Self-reported emotional changes experienced by participants during an interaction with the system are in line with the type of applied profile. Analysis of interaction patterns, i.e., usage of particular dialog act classes, word categories, and textual expressions of affective states for different scenarios, demonstrates that a communication scenario for social sharing of emotions was successfully established. The experimental evidence provides valuable input for applications of affective dialog systems and strengthens them as valid tools for studying affect and social aspects in online communication. [ABSTRACT FROM PUBLISHER]

How do users behave in online chatrooms, where they instantaneously read and write posts? We analyzed about 2.5 million posts covering various topics in Internet relay channels, and found that user activity patterns follow known power-law and stretched exponential distributions, indicating that online chat activity is not different from other forms of communication. Analysing the emotional expressions (positive, negative, neutral) of users, we revealed a remarkable persistence both for individual users and channels. I.e. despite their anonymity, users tend to follow social norms in repeated interactions in online chats, which results in a specific emotional "tone" of the channels. We provide an agent-based model of emotional interaction, which recovers qualitatively both the activity patterns in chatrooms and the emotional persistence of users and channels. While our assumptions about agent's emotional expressions are rooted in psychology, the model allows to test different hypothesis regarding their emotional impact in online communication.