Semantic-enhanced sequential modeling for personality trait recognition from texts

Xue, Xiaomin; Feng, Jun; Sun, Xia

doi:10.1007/s10489-021-02277-7

Cited by 23 publications

(12 citation statements)

References 50 publications

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“…In order to detect user's personality using the structures of texts, the model has been strengthened by a CNN as well as a latent sentence grouping module which has been applied to capture closely connected sentences. Xue et al [43] studied the effects of semantic representation of words in APP systems. They acquired a word-level semantic representation of text elements and then fed them into a neural network to obtain higher-level semantics of text elements.…”

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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“…• Xue et al [80]: they have proposed a semantic-enhanced APP system which acquires hierarchical semantic representations of the text elements.…”

Section: Baseline Modelsmentioning

confidence: 99%

“…KGrAt-Net (KG att.) Ramezani et al [66] El-Demerdash et al [59] Xue et al [80] Ramezani et al [45] Wang et al [20] Kazameini et al [62] Jiang et al [19] El-Demerdash et al [18] Yuan et al [13] Majumder et al [11] Tighe et al [7] 75 KGrAt-Net (KG att.+KG emb.) KGrAt-Net (KG att.)…”

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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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“…SEPRNN (semantic-enhanced personality recognition neural network) [15] is proposed with the goal of avoiding dependency to feature selection in APP and modelling semantic from word-level representations. GloVe plm is deployed to vectorize words, then a BiGRU model learned to extract left and right context of words, but since semantics did not consider.…”

Section: Plm-based Appsmentioning

confidence: 99%

“…With the increasing variety of data types available for analysing the personality of people, aspects of view to APP increases likewise. In this point of view to the assortment of APP, data types can be named as: speech [3][4][5][6], image [7][8][9][10], video [11,12], text [13][14][15], social media activities [16][17][18], touch screen interaction [19,20], and so on. Also, each of these has subsets and divisions of text-based APP which can be mentioned are email [21], SMS [22], and tweets & posts on social media [23].…”

Section: Introductionmentioning

confidence: 99%

Text-based automatic personality prediction: A bibliographic review

Feizi-Derakhshi,

Ramezani

et al. 2021

Preprint

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Personality detection is an old topic in psychology and Automatic Personality Prediction (or Perception) (APP) is the automated (computationally) forecasting of the personality on different types of human generated/exchanged contents (such as text, speech, image, video). The principal objective of this study is to offer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three; pretrained independent, pre-trained model based, multimodal approaches. Also, to achieve a comprehensive comparison, reported results are informed by datasets.

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Semantic-enhanced sequential modeling for personality trait recognition from texts

Cited by 23 publications

References 50 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

Text-based automatic personality prediction: A bibliographic review

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