Design and Development Methodology for the Emotional State Estimation of Verbs

The use of words and particularly the verbs in Human-Human Interaction reveals significant aspects of both human’s social and mental state. This work presents a novel methodology towards the emotional assessment of verbs by users. Essentially we would like to study whether the emotions that user experience are comparable with the corresponding results obtained through a mixture of natural language and statistical classifiers in SentiWordNet. Following the paper and pencil guidelines of the International Affective Picture System (IAPS) we have developed a web-based unsupervised version of the Self Assessment Manikin (SAM) test, designed for the emotional assessment of verbs in English and Greek language. Thirty five men and seventeen women participated in an internet survey version of the experiment. In the first part of the process, the participants had to assess their induced emotional state while reading a verb (totally 75 Greek verbs), on 5-point scales of “Pleasure”, “Arousal” and “Dominance”. The results comprise coherence and consistency. As a rule, all verbs obtained low to mid range scores on Arousal and Dominance axis and only on the Pleasure dimension scores are close to the edge.

[1]  О. В. Смурова «Эпистемическая оценка» как частно-оценочный концепт в английской языковой картине мира (на материале the Corpus of Contemporary American English) , 2013 .

[2]  Shrikanth S. Narayanan,et al.  Combining categorical and primitives-based emotion recognition , 2006, 2006 14th European Signal Processing Conference.

[3]  Shrikanth S. Narayanan,et al.  Primitives-based evaluation and estimation of emotions in speech , 2007, Speech Commun..

[4]  Martin Ebner,et al.  Emotion Detection: Application of the Valence Arousal Space for Rapid Biological Usability Testing to Enhance Universal Access , 2009, HCI.

[5]  M. Bradley,et al.  Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.

[6]  C. Nass,et al.  Emotion in human-computer interaction , 2002 .

[7]  William J. Corulla,et al.  A psychometric investigation of the eysenck personality questionnaire (revised) and its relationship to the I.7 impulsiveness questionnaire , 1987 .

[8]  K. Scherer What are emotions? And how can they be measured? , 2005 .

[9]  Zhigang Deng,et al.  Rigid Head Motion in Expressive Speech Animation: Analysis and Synthesis , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  P. Lang International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[11]  Jon D. Morris Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1 , 1995 .

[12]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[13]  P. Lang,et al.  International Affective Picture System (IAPS): Instruction Manual and Affective Ratings (Tech. Rep. No. A-4) , 1999 .

[14]  J. Jacko,et al.  The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications , 2002 .

[15]  Shrikanth S. Narayanan,et al.  Toward detecting emotions in spoken dialogs , 2005, IEEE Transactions on Speech and Audio Processing.

[16]  G. Bailly,et al.  Editorial Special Section on Expressive Speech Synthesis , 2006 .

[17]  J. Russell,et al.  Evidence for a three-factor theory of emotions , 1977 .

[18]  Constantine Stephanidis,et al.  Universal Access in Human-Computer Interaction , 2011 .

[19]  Shrikanth S. Narayanan,et al.  Support Vector Regression for Automatic Recognition of Spontaneous Emotions in Speech , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[20]  Stephan M. Winkler,et al.  On Text Preprocessing for Opinion Mining Outside of Laboratory Environments , 2012, AMT.

[21]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[22]  Georgios Kouroupetroglou,et al.  Multimodal Accessibility of Documents , 2008 .

[23]  L. Derogatis,et al.  Symptom Checklist‐90‐Revised , 2010 .

[24]  Anastasia Karastergiou,et al.  Standardization of the symptom checklist-90-R rating scale in a Greek population. , 1991 .