Please use this identifier to cite or link to this item: https://doi.org/10.1145/2393347.2393397
Title: Don't ask me what I'm like, just watch and listen
Authors: Srivastava, R.
Feng, J.
Roy, S.
Yan, S. 
Sim, T. 
Keywords: emotion recognition
movie analysis
multimodal features
personality assessment
Issue Date: 2012
Source: Srivastava, R.,Feng, J.,Roy, S.,Yan, S.,Sim, T. (2012). Don't ask me what I'm like, just watch and listen. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia : 329-338. ScholarBank@NUS Repository. https://doi.org/10.1145/2393347.2393397
Abstract: Traditional (based on psychology) approaches for personality assessment of an individual require him/her to fill up a questionnaire. This paper presents a novel way of utilizing multimodal cues to automatically fill up the questionnaire. The contributions of this work are three-fold. (1) Novel psychology-based audio/visual/lexical features are proposed and shown to be effective in predicting answers to a personality questionnaire, Big-Five Inventory-10 (BFI- 10). (2) Extracted features are used to learn linear and kernel versions of a novel regression model, 'SLoT', to automatically predict BFI-10 answers. The model is based on Sparse and Low-rank Transformation (SLoT). (3) Predicted answers are used to compute personality scores using standard BFI-10 scoring scheme. We evaluated our approach on a dataset of 3907 clips (for 50 characters from movies of diverse genres) manually labeled with BFI-10 answers and personality scores as ground-truth. Experiments indicate that the proposed 'SLoT' model effectively automates the answering process by emulating human understanding. We also conclude that predicting personality scores through predicting answers first is better than directly predicting scores based on audio/visual features (as studied in state-of-the art methods). © 2012 ACM.
Source Title: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/43321
ISBN: 9781450310895
DOI: 10.1145/2393347.2393397
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