Psychological Human Traits Detection based on Universal Language Modeling

El-Demerdash, Kamal; El-Khoribi, Reda A.; Shoman, Mahmoud; Abdou, Sherif M.

doi:10.1016/j.eij.2020.09.001

Cited by 7 publications

(12 citation statements)

References 10 publications

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“…At last, the final predictions have taken based on averaging the output of different pre-trained models. Other researches ( [16,[44][45][46]) have also investigated designing embedding-based APP models that make predictions from text.…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani,

Feizi-Derakhshi,

Balafar

2022

Preprint

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How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short * m

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Section: Literature Reviewmentioning

confidence: 99%

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani,

Feizi-Derakhshi,

Balafar

2022

Preprint

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“…A wide diversity of contributions has been published on deep learning-based APP, each of which has a distinct methodology. During last years, an increasing number of studies have investigated the application of embedding methods to transfer the text elements from a textual space to a real valued vector space [14][15][16]18]. The authors in [14], integrated traditional psycholinguistic features such as Mairesse, SenticNet, NRC Emotion Intensity Lexicon, and VAD Lexicon (a lexicon of over 20,000 English words annotated with their valence, arousal and dominance scores), with several language model embeddings, including Bidirectional Encoder Representation from Transformers (BERT), ALBERT (A Lite Biomedical BERT) and RoBERTa (A Robustly Biomedical BERT Approach) to predict personality from the Essays Dataset in Big Five model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• El-Demerdash et al [18]: they have proposed the application of Universal Language Model Fine-Tuning (ULMFiT) for APP which is an effective transfer learning method that can be applied in different language processing tasks.…”

Section: Baseline Modelsmentioning

confidence: 99%

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Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

Ramezani¹,

Feizi‐Derakhshi²,

Balafar³

2022

Preprint

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Nowadays, a tremendous amount of human communications take place on the Internetbased communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate the relationships among them. To this end, this paper aims to propose KGrAt-Net which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, first it tries to acquire a knowingful representation of the knowledge behind the concepts in the input text through building its equivalent knowledge graph. A knowledge graph is a graph-based data model that formally represents the semantics of the existing concepts in the input text and models the knowledge behind them. Then, applying the attention mechanism it efforts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. The results demonstrated that KGrAt-Net considerably improved the personality prediction accuracies. Furthermore, KGrAt-Net also uses the knowledge graphs' embeddings to enrich the classification, which makes it even more accurate in APP.

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“…Kamal El -Demerdash et al [12] have used Universal Language Model Fine-Tuning for personality trait detection on the Big Five model. They have applied this Model on the stream-of-consciousness dataset and they have managed to achieve around 1% better accuracy than most state-of-the-art models.…”

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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Psychological Human Traits Detection based on Universal Language Modeling

Cited by 7 publications

References 10 publications

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

Big Five Personality Detection Using Deep Convolutional Neural Networks

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