Pawel Dybala

CAO: A Fully Automatic Emoticon Analysis System Based on Theory of Kinesics

This paper presents CAO, a system for affect analysis of emoticons in Japanese online communication. Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The presented system extracts emoticons from input and determines the specific emotion types they express with a three-step procedure. First, it matches the extracted emoticons to a predetermined raw emoticon database. The database contains over 10,000 emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing “mouths” or “eyes,” based on the idea of kinemes from the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is first based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides hints about potential groups of expressed emotions, giving the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the systems capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.

Humor Prevails! - Implementing a Joke Generator into a Conversational System

This paper contains the results of evaluation experiments conducted to investigate if implementation of a pun generator into a non-task oriented talking system improves the latters performance. We constructed a simple joking conversational system and conducted one user evaluation experiment and two third person evaluation experiments. The results showed that humor does have a positive influence on the dialogue between humans and computers. The implications of this fact and problems that occurred during the research are discussed. We also propose how they can be solved in the future.

Contextual affect analysis: a system for verification of emotion appropriateness supported with Contextual Valence Shifters

This paper presents a novel method for estimating speakers affective states based on two contextual features: valence shifters and appropriateness. Firstly, a system for affect analysis is used to recognise specific types of emotions. We improve the baseline system with the analysis of Contextual Valence Shifters (CVS), which determine the semantic orientation of emotive expressions. Secondly, a web mining technique is used to verify the appropriateness of the recognised emotions for the particular context. Verification of contextual appropriateness of emotions is the next step towards implementation of Emotional Intelligence Framework in machines. The proposed method is evaluated using two conversational agents.

Serious processing for frivolous purpose: a chatbot using web-mining supported affect analysis and pun generation

By our demonstration we want to introduce our achievements in combining different purpose algorithms to build a chatbot which is able to keep a conversation on any topic. It uses snippets of Internet search results to stay within a context, Nakamuras Emotion Dictionary to detect an emotional load existence and categorization of a textual utterance and a causal consequences retrieval algorithm when emotive features are not found. It is also able to detect a possibility to make a pun by analyzing the input sentence and create one if timing is adequate.

Humorized Computational Intelligence towards User-Adapted Systems with a Sense of Humor

This paper investigates the role of humor in non-task oriented (topic restriction free) human-computer dialogue, as well as the correlation between humor and emotions elicited by it in users. A joke-telling conversational system, constructed for the needs of this research, was evaluated by the users as better and more human-like than a baseline system without humor. Automatic emotive evaluation with the usage of an emotiveness analysis system showed that the system with humor elicited more emotions than the other one, and most of them (almost 80%) were positive. This shows that the presence of humor makes computers easier to familiarize with and simply makes users feel better. Therefore, humor should be taken into consideration in research on user-friendly applications, as it enhances the interaction between user and system. The results are discussed and our concept of a user-adapted humor-equipped system is presented.

Affect as Information about Users' Attitudes to Conversational Agents

This paper presents a novel method for automatic evaluation of conversational agents. In the method, information about userspsila attitudes and sentiments to conversational agents and their performance are achieved by analyzing their general emotional engagement in the conversation and specific affective states, and interpreting them using psychological reasoning of affect-as-information. In the evaluation experiment the userspsila attitudes to two Japanese-speaking conversational agents were checked simultaneously in a survey and using a system constructed on the proposed method. The results returned by the system revealed similar tendencies as the survey. Therefore the method is applicable as a mean of evaluation for Japanese-speaking conversational agents.

Reducing excessive amounts of data: multiple web queries for generation of pun candidates

Humor processing is still a less studied issue, both in NLP and AI. In this paper we contribute to this field. In our previous research we showed that adding a simple pun generator to a chatterbot can significantly improve its performance. The pun generator we used generated only puns based on words (not phrases). In this paper we introduce the next stage of the systems development-- an algorithm allowing generation of phrasal pun candidates. We show that by using only the Internet (without any handmade humor-oriented lexicons), it is possible to generate puns based on complex phrases. As the output list is often excessively long, we also propose a method for reducing the number of candidates by comparing two web-query-based rankings. The evaluation experiment showed that the system achieved an accuracy of 72.5% for finding proper candidates in general, and the reduction method allowed us to significantly shorten the candidates list. The parameters of the reduction algorithm are variable, so that the balance between the number of candidates and the quality of output can be manipulated according to needs.

Towards Computational Fronesis: Verifying Contextual Appropriateness of Emotions

This paper presents research in Contextual Affect Analysis CAA for the need of future application in intelligent agents, such as conversational agents or artificial tutors. The authors propose a new term, Computational Fronesis CF, to embrace the tasks included in CAA applied to development of conversational agents such as artificial tutors. In tutor-student discourse it is crucial that the artificial tutor was able not only to detect user/student emotions, but also to verify toward whom they were directed and whether they were appropriate for the context of the conversation. Therefore, as the first task in CF the authors focus on verification of contextual appropriateness of emotions. They performed some of the first experiments in this task for the Japanese language and discuss future directions in development and implications of Computational Fronesis.

Towards Socialized Machines: Emotions and Sense of Humour in Conversational Agents

From the beginning of computer era over half a century ago, humanity was fascinated by the idea of creating a machine substituting their mental capabilities. This New Age version of Mary Shelley’s Frankenstein gave birth to S-F literature and was one of the motors for development of our civilisation. The mental functions digitalized as the first ones were fast processing of large numbers or sophisticated formulas for specialized fields like mathematics or physics. These functions were the most troublesome for humans, but the easiest to process mechanically. Ironically, the human mental functions said to be the most human-like, and thought of as the ones which make up a grown well-socialized man, such as a sense of humour or understanding emotions of others, were neglected in Computer Science for a long time as too subjective and therefore unscientific. With the development of the Artificial Intelligence research and the related fields, like Human-Computer Interaction (HCI) or Human Factors Design, shortly before the new millennium the door opened to the fields of research of what had been unscientific till then – Affective Computing (Picard, 1997), and Humour Processing (Binsted, 1996). When Kerstin Dautenhahn and colleagues talked about the Socially Intelligent Agents (SIA) on the AAAI Fall Symposium in 2000 (Dautenhahn et al., 2002), they signalised the need for the attempts to incorporate multiple human factors into conversational agents. However, completing the task of creating a userfriendly and human-like machine was still far ahead. In this chapter we present some of the first practical experiments on enhancing Japanese speaking conversational agent with human factors. In our research we focused on the two important features, said to make up an intelligent and socialized man: understanding emotions of others, and a sense of humour to evoke positive attitudes in other people for better socialization (Yip & Martin, 2006). These two features are also said to be the most creative and difficult to process by machines human factors (Boden, 1998). In our research we undertake the task to incorporate these two features in a conversational agent to make it more human like. A conversational agent is enhanced with a pun generator, and a system for affect analysis. The affect analysis system uses a novel method of estimating not only the valence and type of the conveyed emotions, but also, supported with a Web-mining procedure, verifies whether the emotion is appropriate for the present context of the 9

Towards Joking, Humor Sense Equipped and Emotion Aware Conversational Systems

In this paper, we present our progress so far in realization of project aimed to create a complex, modular humor-equipped conversational system. By complex, we mean that it should be able to: (1) detect users’ emotions, (2) detect users’ humorous behaviors and react to them properly, (3) generate humor according to users’ emotive states and (4) learn each user’s individual sense of humor. The research is conducted in Japanese. We chose puns as a relatively computable genre of humor. We describe a general outline of our system, as well as its four modules: humor detection module, emotion recognition module, response generator module and individualisation module. We present the algorithm of systems used in each module, along with some evaluation results.