% % This file was created by the Typo3 extension % sevenpack version 0.7.14 % % --- Timezone: CEST % Creation date: 2013-05-25 % Creation time: 18-51-56 % --- Number of references % 17 % @Article { RuddleVB2013, title = {Learning to Walk in Virtual Reality}, journal = {ACM Transactions on Applied Perception}, year = {2013}, month = {3}, abstract = {This article provides longitudinal data for when participants learned to travel with a walking metaphor through virtual reality (VR) worlds, using interfaces that ranged from joystick-only, to linear and omnidirectional treadmills, and actual walking in VR. Three metrics were used: travel time, collisions (a measure of accuracy), and the speed profile. The time that participants required to reach asymptotic performance for traveling, and what that asymptote was, varied considerably between interfaces. In particular, when a world had tight turns (0.75 m corridors), participants who walked were more proficient than those who used a joystick to locomote and turned either physically or with a joystick, even after 10 minutes of training. The speed profile showed that this was caused by participants spending a notable percentage of the time stationary, irrespective of whether or not they frequently played computer games. The study shows how speed profiles can be used to help evaluate participants‟ proficiency with travel interfaces, highlights the need for training to be structured to addresses specific weaknesses in proficiency (e.g., start-stop movement), and for studies to measure and report that proficiency.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2013/ruddle-acm-tap.pdf}, department = {Department B{\"u}lthoff}, state = {}, author = {Ruddle, RA and Volkova, E and B{\"u}lthoff, HH} } @Article { RuddleVB2011, title = {Walking improves your cognitive map in environments that are large-scale and large in extent}, journal = {ACM Transactions on Computer-Human Interaction}, year = {2011}, month = {6}, volume = {18}, number = {2:10}, pages = {1-22}, abstract = {This study investigated the effect of body-based information (proprioception, etc.) when participants navigated large-scale virtual marketplaces that were either small (Experiment 1) or large in extent (Experiment 2). Extent refers to the size of an environment, whereas scale refers to whether people have to travel through an environment to see the detail necessary for navigation. Each participant was provided with full body-based information (walking through the virtual marketplaces in a large tracking hall or on an omnidirectional treadmill), just the translational component of body-based information (walking on a linear treadmill, but turning with a joystick), just the rotational component (physically turning but using a joystick to translate) or no body-based information (joysticks to translate and rotate). In large and small environments translational body-based information significantly improved the accuracy of participants' cognitive maps, measured using estimates of direction and relative straight line distance but, on its own, rotational body-based information had no effect. In environments of small extent, full body-based information also improved participants' navigational performance. The experiments show that locomotion devices such as linear treadmills would bring substantial benefits to virtual environment applications where large spaces are navigated, and theories of human navigation need to reconsider the contribution made by body-based information, and distinguish between environmental scale and extent.}, department = {Department B{\"u}lthoff}, web_url = {http://portal.acm.org/citation.cfm?doid=1970378.1970384}, DOI = {10.1145/1970378.1970384}, author = {Ruddle, RA and Volkova, E and B{\"u}lthoff, HH} } @Article { RuddleVMB2011, title = {The effect of landmark and body-based sensory information on route knowledge}, journal = {Memory \& Cognition}, year = {2011}, month = {5}, volume = {39}, number = {4}, pages = {686-699}, abstract = {Two experiments investigated the effects of landmarks and body-based information on route knowledge. Participants made four out-and-back journeys along a route, guided only on the first outward trip and with feedback every time an error was made. Experiment 1 used 3-D virtual environments (VEs) with a desktop monitor display, and participants were provided with no supplementary landmarks, only global landmarks, only local landmarks, or both global and local landmarks. Local landmarks significantly reduced the number of errors that participants made, but global landmarks did not. Experiment 2 used a head-mounted display; here, participants who physically walked through the VE (translational and rotational body-based information) made 36\% fewer errors than did participants who traveled by physically turning but changing position using a joystick. Overall, the experiments showed that participants were less sure of where to turn than which way, and journey direction interacted with sensory information to affect the number and types of errors participants made.}, department = {Department B{\"u}lthoff}, web_url = {http://www.springerlink.com/content/12771128x0716033/fulltext.pdf}, DOI = {10.3758/s13421-010-0054-z}, author = {Ruddle, RA and Volkova, E and Mohler, B and B{\"u}lthoff, HH} } @Article { 6952, title = {Experiment for the color idioms project}, journal = {Slovo i Tekst}, year = {2009}, month = {2}, volume = {9}, pages = {157-164}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, language = {en}, author = {Volkova, E} } @Article { 6954, title = {Idioms with basic color terms in the English and Russian languages}, journal = {Vestnik Tverskogo Gosudarstvennogo Universiteta}, year = {2008}, month = {10}, volume = {17}, number = {13}, pages = {202-213}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, language = {en}, author = {Volkova, E} } @Inproceedings { 6682, title = {Virtual Storyteller in Immersive Virtual Environments Using Fairy Tales Annotated for Emotion States}, journal = {Virtual Environments 2010: Joint Virtual Reality Conference of EGVE - 16th Eurographics Symposium on Virtual Environments, EuroVR - the 7th EuroVR (INTUITION) Conference, VEC - the annual Virtual Efficiency Congress}, year = {2010}, month = {10}, pages = {65-68}, abstract = {This paper describes the implementation of an automatically generated virtual storyteller from fairy tale texts which were previously annotated for emotion. In order to gain insight into the effectiveness of our virtual storyteller we recorded face, body and voice of an amateur actor and created an actor animation video of one of the fairy tales. We also got the actor's annotation of the fairy tale text and used this to create a virtual storyteller video. With these two videos, the virtual storyteller and the actor animation, we conducted a user study to determine the effectiveness of our virtual storyteller at conveying the intended emotions of the actor. Encouragingly, participants performed best (when compared to the intended emotions of the actor) when they marked the emotions of the virtual storyteller. Interestingly, the actor himself was not able to annotate the animated actor video with high accuracy as compared to his annotated text. This argues that for future work we must have our actors also annotate their body and facial expressions, not just the text, in order to further investigate the effectiveness of our virtual storyteller. This research is a first step towards using our virtual storyteller in real-time immersive virtual environments.}, url = {http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/Alexandrova_JVRC_authors_version_6682[0].pdf}, department = {Department B{\"u}lthoff}, web_url = {http://www.interaction-design.org/references/conferences/proceedings_of_the_joint_virtual_reality_conference_of_egve_-_eurovr_-_vec.html}, editor = {Kuhlen, T. , S. Coquillart, V. Interrante}, publisher = {Eurographics Association}, address = {Goslar, Germany}, booktitle = {Virtual Environments 2010}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, event_place = {Stuttgart, Germany}, event_name = {2010 Joint Virtual Reality Conference of EuroVR - EGVE - VEC (JVRC 2010)}, language = {en}, ISBN = {978-3-905674-30-9}, DOI = {10.2312/EGVE/JVRC10/065-068}, author = {Alexandrova, IV and Volkova, EP and Kloos, U and B{\"u}lthoff, HH and Mohler, BJ} } @Inproceedings { 6819, title = {Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text}, journal = {Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text}, year = {2010}, month = {6}, pages = {98-106}, abstract = {Emotion analysis (EA) is a rapidly developing area in computational linguistics. An EA system can be extremely useful in fields such as information retrieval and emotion-driven computer animation. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address in this paper is whether the set of emotion categories can be enriched and whether the units to which the categories are assigned can be more flexibly defined. We present an experiment showing how an annotation task can be set up so that untrained participants can perform emotion analysis with high agreement even when not restricted to a predetermined annotation unit and using a rich set of emotion categories. As such it sets the stage for the development of more complex EA systems which are closer to the actual human emotional perception of text.}, url = {http://www.kyb.tuebingen.mpg.de/fileadmin/user_upload/files/publications/NAACL-HLT-2010-Volkova_6819[0].pdf}, department = {Department B{\"u}lthoff}, web_url = {http://www.site.uottawa.ca/\verb=~=diana/naacl2010_EmotionWorkshop.html}, editor = {Inkpen, D. , C. Strapparava}, publisher = {Association for Computational Linguistics}, address = {Morristown, NJ, USA}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, event_place = {Los Angeles, CA, USA}, event_name = {NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text}, language = {en}, author = {Volkova, EP and Mohler, BJ and Meurers, D and Gerdemann, D and B{\"u}lthoff, HH} } @Poster { VolkovaMLAB2011, title = {Contribution of Prosody in Audio-visual Integration to Emotional Perception of Virtual Characters}, journal = {i-Perception}, year = {2011}, month = {10}, volume = {2}, number = {8}, pages = {774}, abstract = {Recent technology provides us with realistic looking virtual characters. Motion capture and elaborate mathematical models supply data for natural looking, controllable facial and bodily animations. With the help of computational linguistics and artificial intelligence, we can automatically assign emotional categories to appropriate stretches of text for a simulation of those social scenarios where verbal communication is important. All this makes virtual characters a valuable tool for creation of versatile stimuli for research on the integration of emotion information from different modalities. We conducted an audio-visual experiment to investigate the differential contributions of emotional speech and facial expressions on emotion identification. We used recorded and synthesized speech as well as dynamic virtual faces, all enhanced for seven emotional categories. The participants were asked to recognize the prevalent emotion of paired faces and audio. Results showed that when the voice was recorded, the vocalized emotion influenced participants’ emotion identification more than the facial expression. However, when the voice was synthesized, facial expression influenced participants’ emotion identification more than vocalized emotion. Additionally, individuals did worse on a identifying either the facial expression or vocalized emotion when the voice was synthesized. Our experimental method can help to determine how to improve synthesized emotional speech.}, department = {Department B{\"u}lthoff}, web_url = {http://imrf.mcmaster.ca/IMRF/ocs3/index.php/imrf/2011/paper/view/263}, web_url2 = {http://i-perception.perceptionweb.com/journal/I/volume/2/article/ic774}, event_place = {Fukuoka, Japan}, event_name = {12th International Multisensory Research Forum (IMRF 2011)}, author = {Volkova, E and Mohler, B and Linkenauger, S and Alexandrova, I and B{\"u}lthoff, HH} } @Poster { VolkovaLABM2011, title = {Integration of Visual and Auditory Stimuli in the Perception of Emotional Expression in Virtual Characters}, journal = {Perception}, year = {2011}, month = {9}, volume = {40}, number = {ECVP Abstract Supplement}, pages = {138}, abstract = {Virtual characters are a potentially valuable tool for creating stimuli for research investigating the perception of emotion. We conducted an audio-visual experiment to investigate the effectiveness of our stimuli to convey the intended emotion. We used dynamic virtual faces in addition to pre-recorded (Burkhardt et al, 2005, Interspeech'2005, 1517–1520) and synthesized speech to create audio-visual stimuli which conveyed all possible combinations of stimuli. Each voice and face stimuli aimed to express one of seven different emotional categories. The participants made judgments of the prevalent emotion. For the pre-recorded voice, the vocalized emotion influenced participants’ emotion judgment more than the facial expression. However, for the synthesized voice, facial expression influenced participants’ emotion judgment more than vocalized emotion. While participants rather accurately labeled (>76\%) the stimuli when face and voice emotion were the same, they performed worse overall on correctly identifying the stimuli when the voice was synthesized. We further analyzed the difference between the emotional categories in each stimulus and found that valence distance in the emotion of the face and voice significantly impacted recognition of the emotion judgment for both natural and synthesized voices. This experimental design provides a method to improve virtual character emotional expression.}, department = {Department B{\"u}lthoff}, web_url = {http://www.perceptionweb.com/abstract.cgi?id=v110451}, event_place = {Toulouse, France}, event_name = {34th European Conference on Visual Perception}, author = {Volkova, E and Linkenauger, S and Alexandrova, I and B{\"u}lthoff, HH and Mohler, B} } @Poster { 7088, title = {Virtual Storytelling of Fairy Tales: Towards Simulation of Emotional Perception of Text}, year = {2010}, month = {10}, volume = {11}, number = {11}, pages = {31}, abstract = {Emotion analysis (EA) is a rapidly developing area in computational linguistics. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address is whether the set of emotion categories can be enriched and whether the units to which the categories are assigned can be more flexibly defined. Six untrained participants annotated a corpus of eight texts having no predetermined annotation units and using fifteen emotional categories. The inter-annotator agreement rates were considerably high for this difficult task: 0.55 (moderate) on average, reaching 0.82 (almost perfect) with some annotator pairs. The final application of the intended EA system is predominantly in the emotion enhancement of human-computer interaction in virtual reality. The system is meant to be a bridge between unprocessed input text and visual and auditory information, like generated speech, facial expressions and body language. The first steps towards integrating text-based information annotated for emotion categories and simulation of human emotional perception of texts in story telling scenarios for virtual reality are already made. We have created a virtual character, whose animation of face and body is driven by annotations in text.}, department = {Department B{\"u}lthoff}, web_url = {http://www.neuroschool-tuebingen-nena.de/fileadmin/user_upload/Dokumente/neuroscience/AbstractbookNeNa2010u.pdf}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, event_place = {Heiligkreuztal, Germany}, event_name = {11th Conference of Junior Neuroscientists of T{\"u}bingen (NeNa 2010)}, language = {en}, author = {Volkova, E} } @Poster { WallravenSMVAVP2010, title = {Understanding Objects and Actions: a VR Experiment}, year = {2010}, month = {9}, pages = {1-2}, abstract = {The human capability to interpret actions and to recognize objects is still far ahead of that of any technical system. Thus, a deeper understanding of how humans are able to interpret human (inter)actions lies at the core of building better artificial cognitive systems. Here, we present results from a first series of perceptual experiments that show how humans are able to infer scenario classes, as well as individual actions and objects from computer animations of everyday situations. The animations were created from a unique corpus of real-life recordings made in the European project POETICON using motion-capture technology and advanced VR programming that allowed for full control over all aspects of the finally rendered data.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2011/JVRC-2010-Wallraven.pdf}, department = {Department B{\"u}lthoff}, web_url = {http://www.interaction-design.org/references/conferences/proceedings_of_the_joint_virtual_reality_conference_of_egve_-_eurovr_-_vec.html}, event_place = {Stuttgart, Germany}, event_name = {2010 Joint Virtual Reality Conference of EuroVR - EGVE - VEC (JVRC 2010)}, author = {Wallraven, C and Schultze, M and Mohler, B and Volkova, E and Alexandrova, I and Vatakis, A and Pastra, K} } @Poster { VolkovaABM2010, title = {Virtual storytelling of fairy tales: Towards simulation of emotional perception of text}, journal = {Perception}, year = {2010}, month = {8}, volume = {39}, number = {ECVP Abstract Supplement}, pages = {31}, abstract = {Emotion analysis (EA) is a rapidly developing area in computational linguistics. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address is whether the set of emotion categories can be enriched and whether the units to which the categories are assigned can be more flexibly defined. Six untrained participants annotated a corpus of eight texts having no predetermined annotation units and using fifteen emotional categories. The inter-annotator agreement rates were considerably high for this difficult task: 0.55 (moderate) on average, reaching 0.82 (almost perfect) with some annotator pairs. The final application of the intended EA system is predominantly in the emotion enhancement of human–computer interaction in virtual reality. The system is meant to be a bridge between unprocessed input text and auditory and visual information: generated speech, animation of facial expressions and body language. The first steps towards integrating text-based information annotated for emotion categories and simulation of human emotional perception of texts in story telling scenarios for virtual reality are already made. We have created a virtual character, whose animation of face and body is driven by annotations in text.}, department = {Department B{\"u}lthoff}, web_url = {http://www.perceptionweb.com/abstract.cgi?id=v100139}, event_place = {Lausanne, Switzerland}, event_name = {33rd European Conference on Visual Perception}, author = {Volkova, EP and Alexandrova, IV and B{\"u}lthoff, HH and Mohler, BJ} } @Thesis { 6821, title = {PETaLS: Perception of Emotions in Text - a Linguistic Simulation}, year = {2010}, month = {10}, abstract = {This thesis explores automatic sentiment analysis techniques with the overall goal of simulating human emotional perception of text. To approach this goal we collect an appropriately sized corpus of GrimmÂ’s fairy tale texts, annotated for a rich set of emotional categories. We employ a full range of existing natural language processing tools for the German language on the collected texts in order to extract a large set of features. Finally, we apply two well-known machine learning algorithms (k-NN and Winnow) to perform various classification tasks on linguistic units as small as short phrases. The results we report are based not only on the pure automatic classification accuracies but also on human evaluation of resulting, machine generated, annotations. One distinctive feature of our approach is its maximal automatization. In order to bring in, analyze and automatically annotate a new text, no manual work is required. As such, we view this work as the first step towards the development of a more complex se ntiment analysis system, which aims to simulate the actual human emotional perception of text.}, department = {Department B{\"u}lthoff}, institute = {Biologische Kybernetik}, organization = {Max-Planck-Gesellschaft}, institution = {Eberhards-Karls-Universit{\"a}t T{\"u}bingen, Germany}, type = {Diplom}, language = {en}, author = {Volkova, EP} } @Conference { VolkovaMB2012, title = {Motion Capture of Emotional Body Language in Narrative Scenarios}, year = {2012}, month = {11}, volume = {13}, pages = {9}, abstract = {We interact with the world we live in by moving in it. The interaction is versatile and includes communications through speech and gestures, which serve as media to transmit ideas and emotions. A narrator, be it a professional actor on the stage or a friend telling an anecdote, expresses her ideas (the content) and feelings (the emotional colouring) through the choice of words and syntactical structures, her prosody, facial expressions and body language. Our present focus is on emotional body language, which became a field of intensive research several decades ago. Before psychopsysical experiments or trajectory analysis can take place, a set of mocap (motion capture) data has to be accumulated. This can be done with different equipment setups and by now human motion can be captured fairly precisely at a high frame rate. One of the major decisions for the researchers however is the choice of scenarios according to which the actors are to perform motion. This question is especially tricky when we deal with emotions, since the problems of sincerity and naturalness come into play. There are several ways to induce emotions and moods in people, but for motion capture the socalled imagination technique has been used most frequently. The actors are asked to evoke an emotion in themselves by recalling a past event. The main drawbacks of this technique in mocap are the following: (1) it is still impossible to ensure that the emotions are sincere and the motion is natural and not artificial or exaggerated; (2) the emotional categories often rapidly succeed each other in random fashion; (3) the emotional scenarios can be very abstract and taken out of context.We have developed an experimental setup where the emotional body language can be captured in a maximally natural yet controlled manner. The participants are asked to imagine they are narrating a fairy-tale to children. They perform several tasks on the text before their acting in recorded. The setup allows the actors to narrate the story at their own pace, move freely and does not require them to learn the text by heart, yet the recorded data can be easily extracted and processed after the motion capture session. The resulting extracted data can then analysed for various features or used in perceptual experiments.}, department = {Department B{\"u}lthoff}, talk_type = {Abstract Talk}, web_url = {http://www.neuroschool-tuebingen-nena.de/}, event_place = {Schramberg, Germany}, event_name = {13th Conference of the Junior Neuroscientists of T{\"u}bingen (NeNA 2012)}, author = {Volkova, EP and Mohler, BJ and B{\"u}lthoff, HH} } @Conference { VolkovaM2012, title = {ePETaLS: Online Annotation Tool for Emotional Text Labelling}, year = {2012}, month = {6}, day = {3}, abstract = {Text annotation is one of the most popular methods of linguistic data collection. The quality of the resulting corpus is one of the major concerns for the researchers, especially when the annotation process is performed by participants who have not received any speci c task-related training. One important factor that can help to ensure high resulting quality is a user-friendly annotation environment. In this talk we present a new annotation system ePETaLS [1] that can help researchers to collect texts annotated for various emotions. Pre-formatted texts can be uploaded onto the system and their annotation can be assigned to a participant, whose task is to mark each phrase in the text with a speci c emotion or leave it neutral. For each phrase, the annotator is also asked to assign the emotional forse and mark the word on which the emotional emphasis falls. Before submission the annotation is checked and the user is informed of any missing values. This step help to ensure higher quality of the resulting texts. The time spent on each annotation is also logged which helps to detect outliers who spend extremely little or too much time on their annotation tasks. The resulting annotation is saved in the XML format and is ready for data extraction. Before an annotation procedure can begin, each text is automatically split into small annotation units. These units correspond to short phrases that people would usually pronounce without pausing when they read the text out loud. Each sentence in the text can contain one and more of such units, a typical unit length is three to seven word tokens. This component of ePETaLS is based on supervised machine learning system TiMBL [2] and uses WebLicht [3] for linguistic data extraction, e.g. lemmas, POS, dependency relation, etc. The machine learning algorithm uses a small corpus of texts that were split into phrases by nave participants. The annotation system is at present used for collecting a corpus of fairy tales in English written down by Andrew Lang [4]. Each text is annotated for ten to thirteen emotions. The nal goal of the project is to create an automatic sentiment analysis system for emotional virtual character animation.}, url = {http://www.kyb.tuebingen.mpg.defileadmin/user_upload/files/publications/2012/TACOS-2012-Volkova.pdf}, department = {Department B{\"u}lthoff}, talk_type = {Abstract Talk}, web_url = {http://tacos.uni-trier.de/?s=timetable}, event_place = {Trier, Germany}, event_name = {22. Tagung der Computerlinguistik-Studierenden (TaCoS 2012)}, author = {Volkova, E and Mstislavski, A} } @Conference { Volkova2011, title = {PETaLS: Perception of Emotions in Text - a Linguistic Simulation}, year = {2011}, month = {6}, abstract = {The emotional aspect is an integral part of human-human interaction. There are few spheres where emotional communication is unnecessary or not desirable. Feelings, opinions and attitudes are often expressed in text, which makes senti- ment analysis (SA) very needed and valuable. Most existing sentiment analysis systems are implemented for and used in specific predefined areas. The applica- tion field could be anything from extracting appraisal expressions (Whitelaw et al., 2005) to opinion mining of customer feedback (Lee et al., 2008). PETaLS, the SA system we have recently developed, has its intended applica- tion in emotion enhancement of human-computer interaction, especially in virtual or augmented reality. The project is based on supervised machine learning and is meant to build a bridge between unprocessed input text and visual and auditory information, coming from the virtual character, like generated speech, facial ex- pressions and body language. This would enable a virtual character to simulate emotional perception and production of text in story telling scenarios. Thus the first step towards building the PETaLS system was to collect a reli- able corpus of texts that are annotated for emotional states, which could later be used for training and testing. We conducted two annotation experiments: a pilot study in which a few selected texts were analyzed by several participants each and a large scale experiment where more than 70 texts were annotated by two participants each. The main goal of the first experiment, which was presented at the previous TaCoS conference (Volkova et al., 2010), is to research the nature of the sentiment analysis performed by humans and to examine whether they can reliably perform the task. The second experiment aimed at collecting of a corpus of reliable data of adequate size for ML training and testing. For both experiments, we chose Brother Grimms fairy tales as the main anno- tation material. One of the challenges was to analyze inter-annotator agreement, which could not be accurately measured with the help of classical methods (Art- stein, 2008) due to the complexity of the annotation task. However, our more flexible approach to annotations comparison showed consistent similarity between annotators as thus proved the acceptable quality of the collected corpora: on av- erage 53\% in the first experiment and 66\% for the second. We used a chain of NLP tools (Schmid, 1994, 1999; Klein \& Manning, 2002; Kunze, 2004; Hinrichs et al., 2009) to analyze the initial texts and to retrieve a large number of linguistic features which then were employed by the TiMBL (Daelemans et al., 2004) for training and testing. A few of the used features, to the best of our knowledge, had never been mentioned in the relevant literature, e.g., finding nouns that occur several times in a story, thus referring to characters and objects that are important for the plot and have a higher chance to trigger emotional states. Classification was performed on short phrases where each classification item was represented by feature values describing the item itself and its closest neigh- bors to the left and to the right. We developed a hierarchy of emotional classes, which assisted deeper understanding of automatic classification problems and in- dicated where the improvements should be made. The accuracy of our classifiers ranges from 47\% to 81\%, depending on the classification task. Each time the major baseline is outperformed and our results are comparable with those of other researches. The most successful approach is when we only distinguish between three emotional categories: positive, negative and neutral, the accuracy for this classification task is 68\%, which is 1.7 higher than the corresponding baseline of 40\%. Since each story can be told in various acceptable ways, we conducted a human test were we applied PETaLS-generated emotion annotations of several texts to the Virtual Story Teller Framework (Alexandrova et al., 2010). They were evaluated by ten participants along with virtual story telling session driven by human annotations. At the current stage of VSTF development, human and computer generated behavior scripts trigger comparable human evaluation responses. We believe that PETaLS, in combination with VSTF, can become a useful tool for automatic animation of virtual characters and thus provide valuable insights in such fields as neuroscience, cognitive psychology and psychophysics.}, department = {Department B{\"u}lthoff}, web_url = {http://www.uni-giessen.de/cms/fbz/fb05/germanistik/iprof/asclhome/tacos2011/prog}, institution = {Max Planck Institute for Biological Cybernetics}, event_place = {Giessen, Germany}, event_name = {21. Tagung der Computerlinguistik Studierenden (TaCoS 2011)}, author = {Volkova, EP} } @Conference { Volkova2010, title = {Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text}, year = {2010}, month = {6}, abstract = {Emotion analysis (EA) is a rapidly developing area in computational linguis- tics. An EA system can be extremely useful in fields such as information retrieval and emotion-driven computer animation. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address is whether the set of emotion categories can be enriched and whether the units to which the categories are as- signed can be more flexibly defined. Ten German native speakers voluntarily participated in the series of experiments we conducted to find answers to these issues. The participants were di- vided into two groups and each participant worked on five of the eight Grimms fairy tales; each text was 1200-1400 words long and written in Standard German. Fifteen emotions categories were used - seven positive: relief, joy, hope, interest, compassion, surprise, approval; seven negative: disturbance, sadness, despair, disgust, hatred, fear, anger; and neutral. The main task for the participants was to locate and mark stretches of text where an emotion was to be conveyed through the speech melody and/or facial expressions if the participant was to read the text out loud. Another task was to annotate the word lists for the fairy tale texts. Other assignments included cognitive task on emotion categories taken outside the fairy tales context, e.g. giving definitions to emotion categories or organizing the categories into clusters. The \(\kappa\) of inter-annotator agreement (IAA) scores were calculated using the emotion clusters information, for according to the results, participants would of- ten stably use different emotions from same clusters at the same stretch of text. Four out of ten participants, two from each group, had very low IAA scores, a high proportion of unmarked text, and they used few emotion categories, so for the evaluation part their data was discarded. The final IAA evaluation was calcu- lated on all the annotation pairs obtained from the six remaining participants. The total number of annotation pairs thus amounted to 48: two texts annotated by all the six annotators, six texts annotated by three annotators for each of the two annotation sets. The annotator agreement was moderate on average (0.53), and some pairs approached the almost perfect IAA rate (0.83). The IAA rates, calculated on the full set of fifteen emotions, without taking the emotion clusters into consideration, gave a moderate IAA rate on average (0.34) and reached substantial level (0.62) at maximum. We consider the resulting IAA rates to be high enough to accept the anno- tations as suitable for gold-standard corpus compilation in the frame of this re- search. As such, we view this work as the first step towards the development of a more complex EA system, which aims to simulate the actual human emotional perception of text. The final goal of our project is an EA system used for emotion enhancement of human-computer interaction in virtual or augmented reality. It is meant to be a bridge between unprocessed input text and visual and auditory information, coming from a virtual character in story telling scenarios.}, department = {Department B{\"u}lthoff}, institution = {Max Planck Institute for Biological Cybernetics}, event_place = {Z{\"u}rich, Switzerland}, event_name = {20. Tagung der Computerlinguistik Studierenden (TaCoS 2010)}, author = {Volkova, EP} }