The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.
Prior to data-driven approaches, personalities were largely measured by asking people questions and assigning traits according to some fixed set of dimensions, such as the Big Five traits of openness, conscientiousness, extraversion, agreeability, and neuroticism @cite_2 . Computational approaches have since advanced to infer these personalities based on observable behaviors such as the actions people take and the language they use @cite_3 . Our work builds on recent advances in neural networks that have been used for natural language processing tasks such as reading comprehension @cite_18 and dialogue modeling and generation @cite_11 @cite_22 @cite_17 . This includes the growing literature in attention mechanisms and memory networks @cite_0 @cite_20 @cite_7 .
@cite_18 @cite_22 @cite_7 @cite_17 @cite_3 @cite_0 @cite_2 @cite_20 @cite_11
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We present an analysis on personality prediction in small groups based on trait attributes from external observers. We use a rich set of automatically extracted audio-visual nonverbal features, including speaking turn, prosodic, visual activity, and visual focus of attention features. We also investigate whether the thin sliced impressions of external observers generalize to the whole meeting in the personality prediction task. Using ridge regression, we have analyzed both the regression and classification performance of personality prediction. Our experiments show that the extraversion trait can be predicted with high accuracy in a binary classification task and visual activity features give higher accuracies than audio ones. The highest accuracy for the extraversion trait, is 75 , obtained with a combination of audio-visual features. Openness to experience trait also has a significant accuracy, only when the whole meeting is used as the unit of processing. This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and or targeting different personality traits. Psychology research suggests that certain personality traits correlate with linguistic behavior. This correlation can be effectively modeled with statistical natural language processing techniques. Prediction accuracy generally improves with larger data samples, which also allows for more lexical features. Most existing work on personality prediction, however, focuses on small samples and closed-vocabulary investigations. Both factors limit the generality and statistical power of the results. In this paper, we explore the use of social media as a resource for large-scale, openvocabulary personality detection. We analyze which features are predictive of which personality traits, and present a novel corpus of 1.2M English tweets annotated with Myers-Briggs personality type and gender. Our experiments show that social media data can provide sufficient linguistic evidence to reliably predict two of four personality dimensions. We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits.