Helmut Prendinger

SentiFul: A Lexicon for Sentiment Analysis

In this paper, we describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy and antonymy relations, hyponymy relations, derivation, and compounding with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening. Besides derivation, we considered important process of finding new words such as compounding, which is a highly productive process, especially in the case of nouns and adjectives. We elaborated the algorithm for automatic extraction of new sentiment-related compounds from WordNet using words from SentiFul as seeds for sentiment-carrying base components and applying the patterns of compound formations. In the paper, the importance of considering modifiers, contextual valence shifters, and modal operators, which are integral parts of the SentiFul lexicon for robust sentiment analysis, is also discussed.

Textual Affect Sensing for Sociable and Expressive Online Communication

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure analysis. The developed Affect Analysis Model was designed to handle not only correctly written text, but also informal messages written in abbreviated or expressive manner. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. In a study based on 160 sentences, the system result agrees with at least two out of three human annotators in 70% of the cases. In order to reflect the detected affective information and social behaviour, an avatar was created.

MPML:a markup language for controlling the behavior of life-like characters

Animated agents have the potential to convey information in a more natural way than other media traditionally used on the world-wide web, such as text, audio, or video clips. They also allow for more natural styles of human–computer interaction than navigation through hypertext documents. In this paper, we will introduce the multi-modal presentation markup language (MPML), which is a powerful and easy-to-use XML-style language enabling content authors to script rich web-based interaction scenarios featuring life-like characters. MPML is a powerful language as it provides controls for the verbal and non-verbal behavior of affective 2D cartoon-style characters, presentation flow, and the integration of external objects, like Java applets. MPML is easy-to-use since we also offer a graphical scripting environment (a visual editor) that facilitates the creation of complex presentation scripts. We will describe the tagging structures of MPML, the MPML3.0 Visual Editor, and illustrate the usefulness of the tagging structures of the language by the actual implementation of a web-based casino where the user may interact with life-like characters. r 2004 Elsevier Ltd. All rights reserved.

Communicating emotions in online chat using physiological sensors and animated text

We present a chat system that uses animateddynamic text associated with emotional information to show the affective state of the user. The system obtains the affective state of a chat user from a physiological sensor attached to the users body. This paper describes preliminary experiments and provides examples of possible applications of our chat system. Observations from informal experiments comparing our animated chat system with a conventional system suggest that an online interface that conveys emotional information helps online users to interact with each other more efficiently.

Affect analysis model: Novel rule-based approach to affect sensing from text

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis

Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writers (positive or negative) sentiment underlying an opinion, and an affect or emotion (e.g. happy, fearful, surprised etc.). We consider sentiment assessment and emotion sensing from text as two different problems, whereby sentiment assessment is a prior task to emotion sensing. This paper presents an approach to sentiment assessment, i.e. the recognition of negative or positive sense of a sentence. We perform semantic dependency analysis on the semantic verb frames of each sentence, and apply a set of rules to each dependency relation to calculate the contextual valence of the whole sentence. By employing a domain-independent, rule-based approach, our system is able to automatically identify sentence-level sentiment. Empirical results indicate that our system outperforms another state-of-the-art approach.

SentiFul: Generating a reliable lexicon for sentiment analysis

The main drawback of any lexicon-based sentiment analysis system is the lack of scalability. Thus, in this paper, we will describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy relations and morphologic modifications with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening.

Emotion estimation and reasoning based on affective textual interaction

This paper presents a novel approach to Emotion Estimation that assesses the affective content from textual messages. Our main goals are to detect emotion from chat or other dialogue messages and to employ animated agents capable of the emotional reasoning based on the textual interaction. In this paper, the emotion estimation module is applied to a chat system, where avatars associated with chat partners act out the assessed emotions of messages through multiple modalities, including synthetic speech and associated affective gestures.

Evaluating affective feedback of the 3d agent max in a competitive cards game

Within the field of Embodied Conversational Agents (ECAs), the simulation of emotions has been suggested as a means to enhance the believability of ECAs and also to effectively contribute to the goal of more intuitive human–computer interfaces. Although various emotion models have been proposed, results demonstrating the appropriateness of displaying particular emotions within ECA applications are scarce or even inconsistent. Worse, questionnaire methods often seem insufficient to evaluate the impact of emotions expressed by ECAs on users. Therefore we propose to analyze non-conscious physiological feedback (bio-signals) of users within a clearly arranged dynamic interaction scenario where various emotional reactions are likely to be evoked. In addition to its diagnostic purpose, physiological user information is also analyzed online to trigger empathic reactions of the ECA during game play, thus increasing the level of social engagement. To evaluate the appropriateness of different types of affective and empathic feedback, we implemented a cards game called Skip-Bo, where the user plays against an expressive 3D humanoid agent called Max, which was designed at the University of Bielefeld [6] and is based on the emotion simulation system of [2]. Work performed at the University of Tokyo and NII provided a real-time system for empathic (agent) feedback that allows one to derive user emotions from skin conductance and electromyography [13]. The findings of our study indicate that within a competitive gaming scenario, the absence of negative agent emotions is conceived as stress-inducing and irritating, and that the integration of empathic feedback supports the acceptance of Max as a co-equal humanoid opponent.

Empathic Embodied Interfaces: Addressing Users’ Affective State

In this paper, we report on our efforts in developing affective character-based interfaces, i.e. interfaces that recognize and measure affective information of the user and address user affect by employing embodied characters. In particular, we describe the Empathic Companion, an animated interface agent that accompanies the user in the setting of a virtual job interview. This interface application takes physiological data (skin conductance and electromyography) of a user in real-time, interprets them as emotions, and addresses the user’s affective states in the form of empathic feedback. We present preliminary results from an exploratory study that aims to evaluate the impact of the Empathic Companion by measuring users’ skin conductance and heart rate.