A Systematic Literature Review of Personality Trait Classification from Textual Content

Ahmad, Hussain; Asghar, Muhammad Zubair; Khan, Alam Sher; Habib, Anam

doi:10.1515/comp-2020-0188

Cited by 36 publications

(20 citation statements)

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“…Feature Selection (FS) aims at including the significant features from the dataset by discarding the insignificant attributes. Resultantly, the selected features have a significant impact on the prediction capability of the model [21].…”

Section: Feature Selectionmentioning

confidence: 99%

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

et al. 2021

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Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

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Section: Feature Selectionmentioning

confidence: 99%

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

et al. 2021

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“…It helps to enhance the accuracy of the text classification process. The reason for applying the data cleaning module is that the user input text in real-world contain a significant amount of noise, so it is needed to clean the data from this noise in order to perform different NLP tasks (text classification) [26]. The data cleaning step is aimed at performing the following tasks on the acquired dataset.…”

Section: Data Cleaningmentioning

confidence: 99%

Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Asghar

Akbar

Asghar

et al. 2021

Computational and Mathematical Methods in Medicine

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Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people’s personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath’s detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath’s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath’s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.

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“…Therefore, collecting such data using this technique can be is very expensive and large-scale datasets with personality traits data collected from questionaries are extremely rare. For this reason, researchers and practitioners are trying to infer customer or user personality traits from other data sources such as social media [20], [21], multiple types of digital footprints [22], user-written texts [23]- [26], or speech and video (e.g., face detection and analysis) [25].…”

Section: Customer Personality Traits Identificationmentioning

confidence: 99%

Contextual Personality-Aware Recommender System Versus Big Data Recommender System

Szmydt

2021

Bus. Inf. Sys.

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Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

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A Systematic Literature Review of Personality Trait Classification from Textual Content

Cited by 36 publications

References 0 publications

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Contextual Personality-Aware Recommender System Versus Big Data Recommender System

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