Baron-Cohen Model Based Personality Classification Using Ensemble Learning

Sood, Ashima; Bhatia, Rekha

doi:10.1007/978-981-10-7641-1_5

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Then, the results have fed into an ensemble learning model that combines multiple classifiers' outputs, to acquire more reliable prediction. Having the same objectives, other researchers ( [47][48][49]) have questioned the usefulness of such an approach.…”

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

View full text Add to dashboard Cite

show abstract

“…At the meantime, there have been a number of studies on how to take the advantages of several classifiers and benefit their prediction abilities simultaneously [45,[59][60][61][62]. It was a great idea.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

Ramezani

Feizi‐Derakhshi

Balafar

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding 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 takes 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 by matching the input text’s concepts with DBpedia knowledge base entries. Then, due to achieving a 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-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

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Baron-Cohen Model Based Personality Classification Using Ensemble Learning

Cited by 4 publications

References 12 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

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

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