PersonalitySensing: A Multi-View Multi-Task Learning Approach for Personality Detection based on Smartphone Usage

Gao, Songcheng; Li, Wenzhong; Song, Lynda Jiwen; Zhang, Xiao; Lin, MingDe; Lu, Sanglu

doi:10.1145/3394171.3413591

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…The typical machine learning algorithms utilised by the reviewed studies were the support vector machine (SVM) [25,30,42,51,52,57,63], random forest [25,31,37,38,57,58,60,65,66], regression analysis [21,24,35,37,60], AdaBoost [37,38,65], Naive Bayes [37,57,65], neural networks [50,62], Bayes net [37,65], probabilistic model [39,40], hidden Markov model [27], Gaussian mixture model [27], latent Dirichlet allocation [28], exponential random graph model [29], decision tree [65], gradient boosted regression trees (GBRT) [59], Kstar [38], LogitBoost [37] and XGBoost [66]. Five studies attempted different machine learning methods, compared their performances and chose the best alternative [25,31,37,...…”

Section: Discussionmentioning

confidence: 99%

“…For call logs, SMS logs, Bluetooth, Wi-Fi, conversations/voices and smartphone usage, descriptive statistics were applied by the reviewed studies. Total number, means, variation, standard deviations and frequencies were calculated from plain numbers [25,26,37,42,43,45,50,52,56,58,59,62,64,65,67].…”

Section: Feature Constructionmentioning

confidence: 99%

“…The reviewed studies also extracted the number of participant contacts from call and SMS logs. Applications were evaluated based on their categories, which were selected manually [26] or according to the classification of the Google Play store [24,62]. Entropy, which measures diversity, unpredictability or irregularity, was also calculated for calls, SMSs, application usage and Bluetooth [35,51,52,58,62,63].…”

Section: Feature Constructionmentioning

confidence: 99%

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Passive social sensing with smartphones: a systematic review

et al. 2022

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Smartphones are widely used hubs of personal communication. With their many sensors, they are capable of monitoring social behaviours. Calls, messages, application usage and even face-to-face conversations can be captured by smartphones. These data are then used to study psychological, behavioural and clinical issues, thus providing an objective and continuous perspective on people’s social lives. However, compared with questionnaires, researchers are unfamiliar with the potential and challenges of smartphone social sensing. Moreover, previous studies utilised their own instruments and strategies, which caused repetitive work and threatened the generalisability or validity of results. To fill the gap between conceptual and empirical knowledge, we conducted a systematic review on empirical studies that applied passive smartphone social sensing. Forty-seven eligible articles were retrieved from 2,741 results from five major databases. A paradigm of reviewed studies was extracted, and the comprehensive procedures of passive smartphone social sensing, including applied sensors, experiment length and data analysis methods, were also summarised. This information can provide a general reference for researchers interested in applying passive smartphone social sensing. Benefits such as ubiquitousness, unobtrusiveness and personalisability and challenges such as privacy, accuracy and methodology were also reported. Moreover, fundamental research on reasonable sensor frequency choices, standardising features and implementing state-of-the-art technologies is recommended to enhance passive smartphone social sensing usability.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Constructionmentioning

confidence: 99%

Section: Feature Constructionmentioning

confidence: 99%

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Passive social sensing with smartphones: a systematic review

et al. 2022

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“…Experiments show that the classification accuracy of deep neural network architecture is higher than that of machine learning algorithms. In view of the multimodality and heterogeneity of smartphone sensing data, Gao et al proposed a deep neural network model to fuse multisource features [14], which performed the classification of Big Five personality in the manner of multitask learning. Experimental results showed that the performance metrics of the proposed approach significantly outperformed shallow machine-learning models.…”

Section: Related Workmentioning

confidence: 99%

Personality Classification of Social Users Based on Feature Fusion

Wang

Sui

Zheng

et al. 2021

Sensors

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Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent. In addition, researchers encode scattered user posts in a sequential or hierarchical manner, ignoring the connection between posts and the unequal value of different posts to classification tasks. We propose a hierarchical hybrid model based on a self-attention mechanism, namely HMAttn-ECBiL, to fully excavate deep semantic information horizontally and vertically. Multiple modules composed of convolutional neural network and bi-directional long short-term memory encode different types of personality representations in a hierarchical and partitioned manner, which pays attention to the contribution of different words in posts and different posts to personality information and captures the dependencies between scattered posts. Moreover, the addition of a word embedding module effectively makes up for the original semantics filtered by a deep neural network. We verified the hybrid model on the MyPersonality dataset. The experimental results showed that the classification performance of the hybrid model exceeds the different model architectures and baseline models, and the average accuracy reached 72.01%.

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“…Songcheng Gao et al in 2020 [10] have used a Multi-View Learning Approach that also includes a Multi-Task approach with StudentLife dataset that they obtains by developing a mobile application. They collected data from a group of 183 individuals from two different universities.…”

Section: Related Workmentioning

confidence: 99%

Big Five Personality Detection Using Deep Convolutional Neural Networks

Tinwala¹,

Rauniyar²

2021

Preprint

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Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

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PersonalitySensing: A Multi-View Multi-Task Learning Approach for Personality Detection based on Smartphone Usage

Cited by 11 publications

References 46 publications

Passive social sensing with smartphones: a systematic review

Passive social sensing with smartphones: a systematic review

Personality Classification of Social Users Based on Feature Fusion

Big Five Personality Detection Using Deep Convolutional Neural Networks

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