The Impact of Affective Verbal Content on Predicting Personality Impressions in YouTube Videos

A Hybrid Approach for Emotion Detection in Support of Affective Interaction

“…Although limited in size, the significance of the recorded sessions lies in the real life experimental setting in which they were obtained. The unified element of our research efforts has been the search for suitable predictive personality and affective models that identify the sound interplay between diverse set of phenomenological and contextual features [32], [33], [34]. The utility of the proposed hybrid method for emotion detection was evaluated in another study investigating the predictive effects of course-and fine-grained affective lexical cues in prediction of personality impressions in YouTube video monologues (vlogs) [34].…”

“…The unified element of our research efforts has been the search for suitable predictive personality and affective models that identify the sound interplay between diverse set of phenomenological and contextual features [32], [33], [34]. The utility of the proposed hybrid method for emotion detection was evaluated in another study investigating the predictive effects of course-and fine-grained affective lexical cues in prediction of personality impressions in YouTube video monologues (vlogs) [34]. The set of audio-visual features provided with the dataset of 404 vlogs made available by the IDIAP Research Institute [35] was extended with: 1) coursegrain affective features, the six Ekman's emotional categories detected by our hybrid method and represented by their 10 http://conceptnet5.media.mit.edu/ valences; and 2) fine-grain valence-related features of the affective words in the form of simple normalized valences and frequencies.…”

“…Inspired by previously published research that demonstrates that acoustic features of speech are predictive of its affective content [31], [32], [33], [34], we are extending our system for affective analysis with a selection of audio features. Machine learning algorithms were trained and tested with a set of three datasets, Electromagnetic Articulography -EMA 11 [32], Database of Polish Emotional Speech 12 [33] and Surrey Audio-Visual Expressed Emotion -SAVEE 13 [34].…”

“…Machine learning algorithms were trained and tested with a set of three datasets, Electromagnetic Articulography -EMA 11 [32], Database of Polish Emotional Speech 12 [33] and Surrey Audio-Visual Expressed Emotion -SAVEE 13 [34]. Early experience of an independent evaluation of our method for affective acoustic analysis of speech with the datasets has paved the way to the recognition that multimodal affective analysis may be able to overcome the shortcomings of the text-only analysis.…”

Combining Deep Facial and Ambient Features for First Impression Estimation

“…Before describing the followed approach, we provide a brief literature review on automatic personality trait recognition. In the past, various approaches have been used for recognizing apparent personality traits from different modalities such as audio [4,5], text [6][7][8] and visual information [9,10]. As in other recognition problems, multimodal systems are also investigated to improve robustness of prediction [11][12][13][14].…”