Exploiting Turn-Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations

Su, Ming-Hsiang; Wu, Chung-Hsien; Zheng, Yuting

doi:10.1109/taslp.2016.2531286

Cited by 22 publications

(12 citation statements)

References 29 publications

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“…With the wider adoption of deep learning techniques and the incremental availability of data and machine resources, novel studies emerged. In [31], Su et al used LIWC to make grammar annotations and they applied it with dialogue transcripts. Deep learning methods translate text into a vectorial space through the computation of the word distribution inside textual documents.…”

Section: Lexical Hypothesis and Nlp In Personality Estimationmentioning

confidence: 99%

Multilingual Transformer-Based Personality Traits Estimation

et al. 2020

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Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media platforms, numerous studies dealt with measuring personality traits by gathering users’ information from their social media profiles. Real world applications showed how natural language processing combined with supervised machine learning algorithms are effective in this field. These applications have some limitations such as focusing on English text only and not considering polysemy in text. In this paper, we propose a multilingual model that handles polysemy by analyzing sentences as a semantic ensemble of interconnected words. The proposed approach processes Facebook posts from the myPersonality dataset and it turns them into a high-dimensional array of features, which are then exploited by a deep neural network architecture based on transformer to perform regression. We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions. We obtained state-of-the-art results in personality traits estimation from social media posts for all five personality traits.

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Section: Lexical Hypothesis and Nlp In Personality Estimationmentioning

confidence: 99%

Multilingual Transformer-Based Personality Traits Estimation

et al. 2020

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“…It was just in recent years that researchers moved towards deep learning. Kalghatgi et al [28] and Su et al [29] used neural networks by feeding them a number of meticulously hand-crafted features, the first about syntax and social behavior while the latter regarding grammar and LIWC annotations extracted from a dialogue. Majumder et al [30] use a CNN to derive a fixed-length feature vector starting from word2vec word embeddings [31], which they extend with eighty four additional features from Mairesse's library [32].…”

Section: Related Workmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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Abstract:We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey.

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“…Recurrent networks are good for taking care of language dependencies. [92] make use of vanilla RNNs for modeling short term temporal evolution in conversation. A 255-dimensional linguistic feature vector is coupled Hidden Markov Model (C-HMM) is employed for detecting the personalities of two speakers across speaker turns in each dialog by using long-term turntaking temporal evolution and cross-speaker contextual information.…”

Section: Textmentioning

confidence: 99%

Recent trends in deep learning based personality detection

et al. 2019

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Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.

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Exploiting Turn-Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations

Cited by 22 publications

References 29 publications

Multilingual Transformer-Based Personality Traits Estimation

Multilingual Transformer-Based Personality Traits Estimation

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

Recent trends in deep learning based personality detection

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