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        <title>A Multi-modal Personality Prediction System</title>
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        <namePart type="given">Chanchal</namePart>
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        <namePart type="given">Aditya</namePart>
        <namePart type="family">Gupta</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pushpak</namePart>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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        <name type="personal">
            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
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    <abstract>Automatic prediction of personality traits has many real-life applications, e.g., in forensics, recommender systems, personalized services etc.. In this work, we have proposed a solution framework for solving the problem of predicting the personality traits of a user from videos. Ambient, facial and the audio features are extracted from the video of the user. These features are used for the final output prediction. The visual and audio modalities are combined in two different ways: averaging of predictions obtained from the individual modalities, and concatenation of features in multi-modal setting. The dataset released in Chalearn-16 is used for evaluating the performance of the system. Experimental results illustrate that it is possible to obtain better performance with a hand full of images, rather than using all the images present in the video</abstract>
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%0 Conference Proceedings
%T A Multi-modal Personality Prediction System
%A Suman, Chanchal
%A Gupta, Aditya
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F suman-etal-2020-multi
%X Automatic prediction of personality traits has many real-life applications, e.g., in forensics, recommender systems, personalized services etc.. In this work, we have proposed a solution framework for solving the problem of predicting the personality traits of a user from videos. Ambient, facial and the audio features are extracted from the video of the user. These features are used for the final output prediction. The visual and audio modalities are combined in two different ways: averaging of predictions obtained from the individual modalities, and concatenation of features in multi-modal setting. The dataset released in Chalearn-16 is used for evaluating the performance of the system. Experimental results illustrate that it is possible to obtain better performance with a hand full of images, rather than using all the images present in the video
%U https://aclanthology.org/2020.icon-main.42
%P 317-322

Markdown (Informal)

[A Multi-modal Personality Prediction System](https://aclanthology.org/2020.icon-main.42) (Suman et al., ICON 2020)

• A Multi-modal Personality Prediction System (Suman et al., ICON 2020)

ACL

• Chanchal Suman, Aditya Gupta, Sriparna Saha, and Pushpak Bhattacharyya. 2020. A Multi-modal Personality Prediction System. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 317–322, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).