Learning Personality Modeling for Regulating Learning Feedback

Shun, Matthias Chan Yong; Yan, Miao Chun; Shen, Zhiqi; Bo, An

doi:10.1109/icalt.2015.118

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…However, this method is subject to user subjectivity. Moreover, it has limitations in participant recruitment, feedback efficiency, and resource consumption [14].Therefore, the self-reporting method needs to be improved.…”

Section: Related Workmentioning

confidence: 99%

SMOTETomek-Based Resampling for Personality Recognition

Wang

Zheng

et al. 2019

IEEE Access

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The main challenge of user personality recognition is low accuracy resulting from small sample size and severe sample distribution imbalance. This paper analyzes the impact of imbalanced data distribution and positive and negative sample overlap on the machine learning classification model. The classification model is based on the data resampling technique, which can improve the classification accuracy. These problems can be solved once the data are effectively resampled. We present a personality prediction method based on particle swarm optimization (PSO) and synthetic minority oversampling technique+Tomek Link (SMOTETomek)resampling (PSO-SMOTETomek), which, apart from effective SMOTETomek resampling of data samples, is able to execute PSO feature optimization for each set of feature combinations. Validated by simulation, our analysis reveals that the PSO-SMOTETomek method is efficient under a small dataset, and the accuracy of personality recognition is improved by up to around 10%. The results are better than those of previous similar studies. The average accuracies of the plain text dataset and the non-plain text dataset are 75.34% and 78.78%, respectively. The average accuracies of the short text dataset and the long text dataset are 75.34% and 64.25%, respectively. From the experimental results, we found that short text has a better classification effect than long text. Plain text data can still have high personality discrimination accuracy, but there is no relevant external information. The proposed model is able to facilitate the design and implementation of a personality recognition system, and the model significantly outperforms existing state-of-the-art models.

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Section: Related Workmentioning

confidence: 99%

SMOTETomek-Based Resampling for Personality Recognition

Wang

Zheng

et al. 2019

IEEE Access

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“…Traditional methods of personality assessment often rely on interviews or self-report scales. This method requires a significant amount of manpower and material resources, but the feedback is limited in quantity and quality [3]. In recent years, deep learning has made significant progress in the field of natural language processing and has become more powerful in text modeling.…”

Section: Introductionmentioning

confidence: 99%

Personality Classification of Social Users Based on Feature Fusion

Wang

Sui

Zheng

et al. 2021

Sensors

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Based on the openness and accessibility of user data, personality recognition is widely used in personalized recommendation, intelligent medicine, natural language processing, and so on. Existing approaches usually adopt a single deep learning mechanism to extract personality information from user data, which leads to semantic loss to some extent. In addition, researchers encode scattered user posts in a sequential or hierarchical manner, ignoring the connection between posts and the unequal value of different posts to classification tasks. We propose a hierarchical hybrid model based on a self-attention mechanism, namely HMAttn-ECBiL, to fully excavate deep semantic information horizontally and vertically. Multiple modules composed of convolutional neural network and bi-directional long short-term memory encode different types of personality representations in a hierarchical and partitioned manner, which pays attention to the contribution of different words in posts and different posts to personality information and captures the dependencies between scattered posts. Moreover, the addition of a word embedding module effectively makes up for the original semantics filtered by a deep neural network. We verified the hybrid model on the MyPersonality dataset. The experimental results showed that the classification performance of the hybrid model exceeds the different model architectures and baseline models, and the average accuracy reached 72.01%.

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Affective Computing Needs Personalization—And a Character?

Reichardt

2020

Character Computing

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Learning Personality Modeling for Regulating Learning Feedback

Cited by 4 publications

References 9 publications

SMOTETomek-Based Resampling for Personality Recognition

SMOTETomek-Based Resampling for Personality Recognition

Personality Classification of Social Users Based on Feature Fusion

Affective Computing Needs Personalization—And a Character?

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