Text-based automatic personality prediction: a bibliographic review

Feizi-Derakhshi, Ali-Reza; Feizi‐Derakhshi, Mohammad‐Reza; Ramezani, Majid; Nikzad-Khasmakhi, Narjes; Asgari-Chenaghlu, Meysam; Akan, Taymaz; Ranjbar-Khadivi, Mehrdad; Zafarni-Moattar, Elnaz; Jahanbakhsh-Nagadeh, Zoleikha

doi:10.1007/s42001-022-00178-4

Cited by 10 publications

(3 citation statements)

References 66 publications

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“…Punctuation may not carry significant discriminatory information for distinguishing between spam and legitimate messages. By removing punctuation, we focus on the content and meaning of the messages, simplifying the data representation and reducing the vocabulary size [39]. 2) Lowercasing: To ensure uniformity and consistency in our data, we convert all words in the text to lowercase.…”

Section: A Preprocessingmentioning

confidence: 99%

Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection

Al-Kabbi,

Feizi-Derakhshi,

Pashazadeh

2023

IEEE Access

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SMS spam is a pervasive issue that affects millions worldwide, leading to significant inconvenience, time wastage, and potential financial scams. Given the prevalence and potential harm, accurate and real-time detection of SMS spam is crucial. This paper proposes a novel approach to SMS spam detection involving five steps: preprocessing, feature extraction, feature fusion, feature selection, and classification. Our model is designed to simultaneously capture local, temporal, and global text message features using a hybrid deep learning model to enhance feature representation. We evaluated our model using the UCI dataset, comparing it with traditional and deep learning algorithms such as RF and BERT using cross-validation to ensure the robustness of our results. Our proposed method exhibited superior performance, achieving a good accuracy of 99.56%, surpassing other methods. The effectiveness of this method in SMS spam detection proved its potential for real-world implementation, where it could substantially mitigate the prevalence and impact of SMS spam.

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Section: A Preprocessingmentioning

confidence: 99%

Multi-Type Feature Extraction and Early Fusion Framework for SMS Spam Detection

Al-Kabbi,

Feizi-Derakhshi,

Pashazadeh

2023

IEEE Access

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“…To clarify it, indicating some facets of each category may be helpful (please refer to Refs. 39 , 40 for more details): Openness (O) an inclination to embrace new ideas, arts, feelings, and behaviours; unconventional; focused on tackling new challenge; wide range of interests and so imaginative. Conscientiousness (C) self-disciplined, well organized and dutiful; careful and hard-working; reliable, resourceful and on time.…”

Section: Personality Assessment In Psychologymentioning

confidence: 99%

“…To clarify it, indicating some facets of each category may be helpful (please refer to Refs. 39,40 for more details):…”

Section: Personality Assessment In Psychologymentioning

confidence: 99%

Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier

Ramezani

Feizi‐Derakhshi

Balafar

2022

Sci Rep

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Nowadays, a tremendous amount of human communications occur on Internet-based 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 their relationships. 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, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.

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