scholarly journals Modulation of Response Times During Processing of Emotional Body Language

2021 ◽  
Vol 12 ◽  
Author(s):  
Alessandro Botta ◽  
Giovanna Lagravinese ◽  
Marco Bove ◽  
Alessio Avenanti ◽  
Laura Avanzino
Keyword(s):  
Language Processing ◽  
Response Times ◽  
Item Analysis ◽  
Body Language ◽  
Emotional Content ◽  
Motor Resonance ◽  
Body Postures ◽  
Long Time ◽  
Forced Choice Task ◽  
Valence And Arousal

The investigation of how humans perceive and respond to emotional signals conveyed by the human body has been for a long time secondary compared with the investigation of facial expressions and emotional scenes recognition. The aims of this behavioral study were to assess the ability to process emotional body postures and to test whether motor response is mainly driven by the emotional content of the picture or if it is influenced by motor resonance. Emotional body postures and scenes (IAPS) divided into three clusters (fear, happiness, and neutral) were shown to 25 healthy subjects (13 males, mean age ± SD: 22.3 ± 1.8 years) in a three-alternative forced choice task. Subjects were asked to recognize the emotional content of the pictures by pressing one of three keys as fast as possible in order to estimate response times (RTs). The rating of valence and arousal was also performed. We found shorter RTs for fearful body postures as compared with happy and neutral postures. In contrast, no differences across emotional categories were found for the IAPS stimuli. Analysis on valence and arousal and the subsequent item analysis showed an excellent reliability of the two sets of images used in the experiment. Our results show that fearful body postures are rapidly recognized and processed, probably thanks to the automatic activation of a series of central nervous system structures orchestrating the defensive threat reactions, strengthening and supporting previous neurophysiological and behavioral findings in body language processing.

Science of Emoticons

2012 ◽  
pp. 234-260 ◽  
Author(s):  
Michal Ptaszynski ◽  
Jacek Maciejewski ◽  
Pawel Dybala ◽  
Rafal Rzepka ◽  
Kenji Araki ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
System Performance ◽  
State Of The Art ◽  
Body Language ◽  
Linguistic Information ◽  
Long Time ◽  
History Of

Emoticons are string of symbols representing body language in text-based communication. For a long time they have been considered as unnatural language entities. This chapter argues that, in over 40-year-long history of text-based communication, emoticons have gained a status of an indispensable means of support for text-based messages. This makes them fully a part of Natural Language Processing. The fact the emoticons have been considered as unnatural language expressions has two causes. Firstly, emoticons represent body language, which by definition is nonverbal. Secondly, there has been a lack of sufficient methods for the analysis of emoticons. Emoticons represent a multimodal (bimodal in particular) type of information. Although they are embedded in lexical form, they convey non-linguistic information. To prove this argument the authors propose that the analysis of emoticons was based on a theory designed for the analysis of body language. In particular, the authors apply the theory of kinesics to develop a state of the art system for extraction and analysis of kaomoji, Japanese emoticons. The system performance is verified in comparison with other emoticon analysis systems. Experiments showed that the presented approach provides nearly ideal results in different aspects of emoticon analysis, thus proving that emoticons possess features of multimodal expressions.

scholarly journals How does the robot feel? Perception of valence and arousal in emotional body language

2018 ◽  
Vol 9 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Mina Marmpena ◽  
Angelica Lim ◽  
Torbjørn S. Dahl
Keyword(s):  
Human Perception ◽  
Ground Truth ◽  
Body Language ◽  
Human Robot Interaction ◽  
Behavioral Experiments ◽  
Robot Interaction ◽  
Expression Of Emotion ◽  
Exploratory Approach ◽  
Starting Point ◽  
Valence And Arousal

Abstract Human-robot interaction in social robotics applications could be greatly enhanced by robotic behaviors that incorporate emotional body language. Using as our starting point a set of pre-designed, emotion conveying animations that have been created by professional animators for the Pepper robot, we seek to explore how humans perceive their affect content, and to increase their usability by annotating them with reliable labels of valence and arousal, in a continuous interval space. We conducted an experiment with 20 participants who were presented with the animations and rated them in the two-dimensional affect space. An inter-rater reliability analysis was applied to support the aggregation of the ratings for deriving the final labels. The set of emotional body language animations with the labels of valence and arousal is available and can potentially be useful to other researchers as a ground truth for behavioral experiments on robotic expression of emotion, or for the automatic selection of robotic emotional behaviors with respect to valence and arousal. To further utilize the data we collected, we analyzed it with an exploratory approach and we present some interesting trends with regard to the human perception of Pepper’s emotional body language, that might be worth further investigation.

scholarly journals The Effects of Emotional Content on Phonological Processing in Children Who Stutter

2020 ◽  
Vol 18 (4) ◽  
pp. 431-444
Author(s):  
Sousan Salehi ◽  
◽  
Ahmad Reza Khatoonabadi ◽  
Mahmoud Reza Ashrafi ◽  
Ghasem Mohammadkhani ◽  
...  
Keyword(s):  
Reaction Time ◽  
Language Processing ◽  
Phonological Processing ◽  
Repeated Measures ◽  
Event Related Potential ◽  
Emotional Content ◽  
Time Range ◽  
High Time Resolution ◽  
Similar Distribution ◽  
Emotional Words

Objectives: Stuttering and phonological processing are mutually related. Emotion is an effective factor in fluency and language processing; however, its underlying neural mechanism remains unclear. Event-Related Potential (ERP) is a non-invasive highly-beneficial method with high time resolution for language processing. The present study aimed to explore phonological processing in emotional words in Children Who Stutter (CWS), compared to Typically-Developing Children (TDC). Methods: Ten Persian-speaking CWS (3 girls, 7 boys), aged 7-10 years (Mean±SD = 8.9±0.11) and 10 TDC who are matched in age (Mean±SD = 8.7±0.12) and gender were given 120 emotional words (high-valence low-valence) and neutral words to read. Phonological processing was measured by the aloud reading task, while ERP was simultaneously recorded. The collected results were analyzed as behavioral (reaction time and reading accuracy) and electrophysiological (amplitude and topography). Repeated-measures Analysis of Variance (ANOVA) and Independent Samples t-test were used for between-group comparisons. Results: The obtained behavioral data included Reaction Time (RT) and accuracy. There were no significant differences between the explored CWS and TDC in RT and accuracy (P>0.05). The mean value of amplitudes presented significant differences between CWS and TDC in language processing areas (P<0.05). The collected results indicated higher mean values of amplitude for neutral words. The distribution highly differed between the investigated CWS and TDC in neutral and negative words. However, there were similarities in positive words in distribution between the study groups. Discussion: The studied CWS and TDC were similar concerning behavioral results. High-valence words in the CWS group presented a higher similar distribution, compared to the TDC groups; however, low-valence words in the explored CWS had a more similar amplitude to the TDC group for neutral words. Then, emotional content facilitated phonological processing in the investigated CWS in the given time range.

scholarly journals Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

2021 ◽  
Vol 297 ◽  
pp. 01071
Author(s):  
Sifi Fatima-Zahrae ◽  
Sabbar Wafae ◽  
El Mzabi Amal
Keyword(s):  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Sentiment Classification ◽  
Research Areas ◽  
Preprocessing Method ◽  
Long Time ◽  
Standard Text ◽  
The Given ◽  
Dirichlet Allocation

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.

scholarly journals Emotional aspects of language

ANALES RANM ◽  
2018 ◽  
Vol 135 (135(02)) ◽  
pp. 41-46
Author(s):  
J.A. Hinojosa ◽  
E.M. Moreno ◽  
P. Ferré ◽  
M.A. Pozo
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Developmental Psychology ◽  
Brain Regions ◽  
Emotional Content ◽  
Current Work ◽  
Subcortical Structures ◽  
Emotional Aspects ◽  
The Relationship ◽  
And Linguistics

Up to date the study of the relationship between language and emotion has received little attention from researchers. In the current work we will summarize evidence coming from the fields of developmental psychology and affective neurolinguistics. The results from different studies indicate that learning emotional language has its own idiosyncrasy. Also, the emotional content of words, sentences and texts modulates several levels of language processing, including phonological, lexico-semantic and morpho-syntactic aspects of language comprehension and production. Finally, the interactions between language and emotion involve the activation of several brain regions linked to distinct affective and linguistics processes, like parts of frontal and temporal cortices or subcortical structures such as the amygdala. Overall, the results of these studies clearly show that emotional content determines certain aspects of how we acquire and process language.

The Importance of Tone and Attitude in Email and the Online Classroom

2020 ◽  
pp. 52-67
Author(s):  
Melissa A. Miller
Keyword(s):  
Response Times ◽  
Body Language ◽  
Ease Of Use ◽  
Rapid Response ◽  
Communication Tool ◽  
Online Classroom ◽  
Sensory Stimuli ◽  
Communication Method ◽  
Email Correspondence

In the online classroom, email has emerged as a predominant communication method between students and faculty. Despite many benefits of email, including ease of use, familiarity of the technology, and rapid response times, there are numerous challenges faculty face when sending and receiving email correspondence with students. Mainly, due to the medium and format of email, with its lack of cues such as body language, inflection, and other sensory stimuli, it presents a paramount challenge to faculty. However, appropriate tone and attitude in emails can help mitigate the challenges the medium presents. When written and read effectively and purposefully, email is an effective outreach and communication tool for students and faculty.

scholarly journals Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

2021 ◽  
Author(s):  
Małgorzata Wierzba ◽  
Monika Riegel ◽  
Jan Kocoń ◽  
Piotr Miłkowski ◽  
Arkadiusz Janz ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Semantic Network ◽  
Word Meanings ◽  
Linguistic Resources ◽  
Direct Mapping ◽  
Valence And Arousal ◽  
Princeton Wordnet ◽  
Multiple Languages ◽  
Summary Data

AbstractEmotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

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