G. Farnadi, Shanu Sushmita, Geetha Sitaraman, Nhat Ton, M. D. Cock, S. Davalos
Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as well as textual (emotional and linguistic) features extracted from the transcripts of vlogs. Based on these features, we predict the extent to which the video blogger is perceived to exhibit each of the traits of the Big Five personality model. In addition, we explore 5 multivariate regression techniques and contrast them with a single target approach for predicting personality impression scores. All 6 algorithms are able to outperform the average baseline model for all 5 personality traits on a dataset of 404 YouTube videos. This is interesting because previously published methods for the same dataset show an improvement over the baseline for the majority of personality traits, but not for all simultaneously.
{"title":"A Multivariate Regression Approach to Personality Impression Recognition of Vloggers","authors":"G. Farnadi, Shanu Sushmita, Geetha Sitaraman, Nhat Ton, M. D. Cock, S. Davalos","doi":"10.1145/2659522.2659526","DOIUrl":"https://doi.org/10.1145/2659522.2659526","url":null,"abstract":"Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as well as textual (emotional and linguistic) features extracted from the transcripts of vlogs. Based on these features, we predict the extent to which the video blogger is perceived to exhibit each of the traits of the Big Five personality model. In addition, we explore 5 multivariate regression techniques and contrast them with a single target approach for predicting personality impression scores. All 6 algorithms are able to outperform the average baseline model for all 5 personality traits on a dataset of 404 YouTube videos. This is interesting because previously published methods for the same dataset show an improvement over the baseline for the majority of personality traits, but not for all simultaneously.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132681498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chandrima Sarkar, S. Bhatia, Arvind Agarwal, Juan Li
It is an important yet challenging task to develop an intelligent system in a way that it automatically classifies human personality traits. Automatic classification of human traits requires the knowledge of significant attributes and features that contribute to the prediction of a given trait. Motivated by the fact that detection of significant features is an essential part of a personality recognition system, we present in this paper an in-depth analysis of audio visual, text, demographic and sentiment features for classification of multi-modal personality traits namely, extraversion, agreeableness, conscientiousness, emotional stability and openness to experience. We use the YouTube personality data set and use logistic regression model with a ridge estimator for the classification purpose. We experiment with audio-visual features, bag of word features, sentiment based and demographic features. Our results provide important insights about the significance of different feature types for personality classification task.
{"title":"Feature Analysis for Computational Personality Recognition Using YouTube Personality Data set","authors":"Chandrima Sarkar, S. Bhatia, Arvind Agarwal, Juan Li","doi":"10.1145/2659522.2659528","DOIUrl":"https://doi.org/10.1145/2659522.2659528","url":null,"abstract":"It is an important yet challenging task to develop an intelligent system in a way that it automatically classifies human personality traits. Automatic classification of human traits requires the knowledge of significant attributes and features that contribute to the prediction of a given trait. Motivated by the fact that detection of significant features is an essential part of a personality recognition system, we present in this paper an in-depth analysis of audio visual, text, demographic and sentiment features for classification of multi-modal personality traits namely, extraversion, agreeableness, conscientiousness, emotional stability and openness to experience. We use the YouTube personality data set and use logistic regression model with a ridge estimator for the classification purpose. We experiment with audio-visual features, bag of word features, sentiment based and demographic features. Our results provide important insights about the significance of different feature types for personality classification task.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes our submission for the WCPR14 shared task on computational personality recognition. We have investigated whether the features proposed by Soler and Wanner (2014) for gender prediction might also be useful in personality recognition. We have compared these features with simple approaches using token unigrams, character trigrams and liwc features. Although the newly investigated features seem to work quite well on certain personality traits, they do not outperform the simple approaches.
{"title":"Evaluating Content-Independent Features for Personality Recognition","authors":"B. Verhoeven, Juan Soler, Walter Daelemans","doi":"10.1145/2659522.2659527","DOIUrl":"https://doi.org/10.1145/2659522.2659527","url":null,"abstract":"This paper describes our submission for the WCPR14 shared task on computational personality recognition. We have investigated whether the features proposed by Soler and Wanner (2014) for gender prediction might also be useful in personality recognition. We have compared these features with simple approaches using token unigrams, character trigrams and liwc features. Although the newly investigated features seem to work quite well on certain personality traits, they do not outperform the simple approaches.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117026325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic classification of personality from language depends upon large quantities of relevant training data, which raises two potential problems. First, collecting personality information from the author or speaker can be invasive and expensive, especially in sensitive contexts. Second, issues of context or genre can reduce the usefulness of available training resources for broader personality classification. One approach to dealing with the first issue is to use external judges rather than the text's author. In this paper, we test the extent to which these personality perceptions are useful for training a classifier between different linguistic genres. Following disappointing cross-training results, we explore the projection of personality through specific linguistic factors. We find that while some differences are between the genres overall, some indicate that indeed personality is evidenced differently across situations. It is clear that care is needed leveraging resources from different domains for computational personality recognition.
{"title":"Look! Who's Talking?: Projection of Extraversion Across Different Social Contexts","authors":"Scott Nowson, Alastair J. Gill","doi":"10.1145/2659522.2659530","DOIUrl":"https://doi.org/10.1145/2659522.2659530","url":null,"abstract":"Automatic classification of personality from language depends upon large quantities of relevant training data, which raises two potential problems. First, collecting personality information from the author or speaker can be invasive and expensive, especially in sensitive contexts. Second, issues of context or genre can reduce the usefulness of available training resources for broader personality classification. One approach to dealing with the first issue is to use external judges rather than the text's author. In this paper, we test the extent to which these personality perceptions are useful for training a classifier between different linguistic genres. Following disappointing cross-training results, we explore the projection of personality through specific linguistic factors. We find that while some differences are between the genres overall, some indicate that indeed personality is evidenced differently across situations. It is clear that care is needed leveraging resources from different domains for computational personality recognition.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122741753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human nature as always implies massive challenges for predictive modeling that are yet to be fully explored. In this paper, we report on an experiment that examines the predictive effect of the gender and the affective content of video transcripts on predicting personality impressions of the person being judged. While gender had positive impact on the predictability across all personality traits, the effects of the emotional features varied across traits. Coarse-grain emotional categories resulted in performance gains for Agreeableness and Neuroticism, while the inclusion of fine-grain emotional features had more positive effect on predicting Extroversion and Openness to Experiences. The initial results are encouraging and comparable to related research.
{"title":"The Impact of Affective Verbal Content on Predicting Personality Impressions in YouTube Videos","authors":"S. Gievska, Kiril Koroveshovski","doi":"10.1145/2659522.2659529","DOIUrl":"https://doi.org/10.1145/2659522.2659529","url":null,"abstract":"Human nature as always implies massive challenges for predictive modeling that are yet to be fully explored. In this paper, we report on an experiment that examines the predictive effect of the gender and the affective content of video transcripts on predicting personality impressions of the person being judged. While gender had positive impact on the predictability across all personality traits, the effects of the emotional features varied across traits. Coarse-grain emotional categories resulted in performance gains for Agreeableness and Neuroticism, while the inclusion of fine-grain emotional features had more positive effect on predicting Extroversion and Openness to Experiences. The initial results are encouraging and comparable to related research.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Measuring personality traits has a long story in psychology where analysis has been done by asking sets of questions. These question sets (inventories) have been designed by investigating lexical terms that we use in our daily communications or by analyzing biological phenomena. Whether consciously or unconsciously we express our thoughts and behaviors when communicating with others, either verbally, non-verbally or using visual expressions. Recently, research in behavioral signal processing has focused on automatically measuring personality traits using different behavioral cues that appear in our daily communication. In this study, we present an approach to automatically recognize personality traits using a video-blog (vlog) corpus, consisting of transcription and extracted audio-visual features. We analyzed linguistic, psycholinguistic and emotional features in addition to the audio-visual features provided with the dataset. We also studied whether we can better predict a trait by identifying other traits. Using our best models we obtained very promising results compared to the official baseline.
{"title":"Predicting Personality Traits using Multimodal Information","authors":"Firoj Alam, G. Riccardi","doi":"10.1145/2659522.2659531","DOIUrl":"https://doi.org/10.1145/2659522.2659531","url":null,"abstract":"Measuring personality traits has a long story in psychology where analysis has been done by asking sets of questions. These question sets (inventories) have been designed by investigating lexical terms that we use in our daily communications or by analyzing biological phenomena. Whether consciously or unconsciously we express our thoughts and behaviors when communicating with others, either verbally, non-verbally or using visual expressions. Recently, research in behavioral signal processing has focused on automatically measuring personality traits using different behavioral cues that appear in our daily communication. In this study, we present an approach to automatically recognize personality traits using a video-blog (vlog) corpus, consisting of transcription and extracted audio-visual features. We analyzed linguistic, psycholinguistic and emotional features in addition to the audio-visual features provided with the dataset. We also studied whether we can better predict a trait by identifying other traits. Using our best models we obtained very promising results compared to the official baseline.","PeriodicalId":423934,"journal":{"name":"WCPR '14","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116751155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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