Knowledge of words: An interpretable approach for personality recognition from social media

Han, Songqiao; Huang, Hailiang; Tang, Yuqing

doi:10.1016/j.knosys.2020.105550

Cited by 46 publications

(20 citation statements)

References 19 publications

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“…In fact, earlier studies (e.g. [4,11,16,18,24,25]) demonstrate that there is a strong correlation between user personalities and their online behavior on social media. Some examples of applications that can take advantage from the user personality information include recruitment systems, personal counseling systems, online marketing, personal recommendation systems, and bank credit scoring systems to name a few [5,12].…”

Section: Introductionmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

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The ever-increasing social media users has dramatically contributed to significant growth as far as the volume of online information is concerned. Often, the contents that these users put in social media can give valuable insights on their personalities (e.g., in terms of predicting job satisfaction, specific preferences, as well as the success of professional and romantic relationship) and getting it without the hassle of taking formal personality test. Termed personality prediction, the process involves extracting the digital content into features and mapping it according to a personality model. Owing to its simplicity and proven capability, a well-known personality model, called the big five personality traits, has often been adopted in the literature as the de facto standard for personality assessment. To date, there are many algorithms that can be used to extract embedded contextualized word from textual data for personality prediction system; some of them are based on ensembled model and deep learning. Although useful, existing algorithms such as RNN and LSTM suffers from the following limitations. Firstly, these algorithms take a long time to train the model owing to its sequential inputs. Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. To address these aforementioned limitations, this paper introduces a new prediction using multi model deep learning architecture combined with multiple pre-trained language model such as BERT, RoBERTa, and XLNet as features extraction method on social media data sources. Finally, the system takes the decision based on model averaging to make prediction. Unlike earlier work which adopts a single social media data with open and close vocabulary extraction method, the proposed work uses multiple social media data sources namely Facebook and Twitter and produce a predictive model for each trait using bidirectional context feature combine with extraction method. Our experience with the proposed work has been encouraging as it has outperformed similar existing works in the literature. More precisely, our results achieve a maximum accuracy of 86.2% and 0.912 f1 measure score on the Facebook dataset; 88.5% accuracy and 0.882 f1 measure score on the Twitter dataset.

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Section: Introductionmentioning

confidence: 99%

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

et al. 2021

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“…In the process of automated metaprogram detection and personality type prediction based on MBTI personality type indicators, Amirhosseini et al [8] used a new machine learning method developed with the natural language processing toolkit and XGBoost. Han et al [9] proposed a personality recognition model based on personality lexicon, which analyzed relationships between semantic categories of user microblogs and personality scores and used machine learning classifier for recognition task.…”

Section: Related Workmentioning

confidence: 99%

“…Each sequence fragment V c selectively forgets or remembers the information in the context cell state through Bi-LSTM, so that information useful for cell state calculations can be transmitted, while useless information is discarded, and the hidden layer state h t c will be output at each time step. The word vector of the input layer will be calculated in both forward and backward directions, and the hidden state of the final output will be connected to obtain a new sentence vector, as shown in Equation (9).…”

mentioning

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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“…Recently, researchers focused on studying how the personality traits are manifested on social networks. Such researches proved the presence of relationships between the Big Five traits and traditional features extracted from users" generated data (i.e., text and images) [6][7][8]. On the same side, many psychological studies managed to examine the relationships between the personality traits and some psychological characteristics.…”

Section: Introductionmentioning

confidence: 99%

Extracting Relationships Between Big Five Model and Personality Characteristics in Social Networks

Hassanein

Rady

Hussein

et al. 2021

IJICIS

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Recently, researches focused on studying how the Big Five personality traits are manifested on social networks. These researches proved the presence of relationships between the Big Five Personality traits and various social networks features extracted from users' generated content. In this paper, the relationships between the Big Five personality traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and attributes of personality characteristics identified as the Personal Values and Human Needs are studied. These attributes or namely features, are extracted from users' posts on social media. The relationship between the traits and proposed attributes is investigated through Pearson correlation coefficients. A dataset for 564 Twitter users is used in an experimental study, where findings proved the presence of relevant correlations between the traits and the proposed personality characteristic features. The Conscientiousness, Agreeableness, and Neuroticism traits showed strong relations existence with all of the Personal Values features, while the Openness to experience and Neuroticism traits showed strong correlations with Liberty and Selfexpression Needs features consecutively. The proposed study verified the effectiveness of the proposed Personal Values and Human Needs features as indicators for the Big Five personality traits, proving their ability for personality characteristics classification.

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Knowledge of words: An interpretable approach for personality recognition from social media

Cited by 46 publications

References 19 publications

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Personality Classification of Social Users Based on Feature Fusion

Extracting Relationships Between Big Five Model and Personality Characteristics in Social Networks

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