Personality Traits Prediction Using DISC

Abstract

This research introducesanovelmethodof optimizing the process of cultural fit assessment in corporate recruitment through the combination of naturallanguageprocessing(NLP)methodsand semi-supervised self-training models. Using an unlabeled dataset drawn from tweets, we used preprocessing techniques and word2vec embeddings to determine semantic relationships in the text. K-means clustering was used to find the optimal number of clusters (k=4). In cooperation with psychology professionals, we marked 10% of the dataset based on the DISC model of psychology to facilitate model training. A mixed model integrating Convolutional Neural Networks(CNN)andLongShort-TermMemory networks (LSTM) was created to properly leverage labeled and unlabeled data via semi-supervisedlearning. This study will simplify culturalfitevaluation during recruitment, making it possible to quickly and accurately evaluate candidates.

Introduction

The modern recruitment process faces major challenges due to the surge in job applications, which overwhelms traditional face-to-face interviews and cultural fit evaluations. Previous studies have explored personality prediction from social media data but had limitations, such as relying on simpler models or focusing only on the Big Five personality traits.

This research proposes a more advanced approach using semi-supervised learning and the DISC personality model to automate personality assessment from social media behavior, specifically Twitter data. The study uses natural language processing (NLP) techniques like Word2Vec to convert tweets into numerical data, applies K-means clustering to identify patterns, and involves psychology experts labeling a small portion of data. A hybrid neural network combining CNN and LSTM processes both labeled and unlabeled data iteratively to improve personality classification accuracy.

This method aims to enhance recruitment by providing more precise and efficient candidate screening based on personality traits, offering a sophisticated alternative to conventional cultural fit tests.

The related works section reviews prior methods:

• Using social media features and random forest regression for personality prediction.

• Naive Bayes and KNN algorithms applied to Big Five traits from Twitter data.

• Hybrid CNN-RNN models improving accuracy in personality detection.

• NLP techniques like SciBERT and K-means for scientific text classification.

The methodology details data preprocessing (cleaning and lemmatization of tweets), model architecture (Word2Vec embeddings, K-means clustering with elbow method to find optimal clusters, and a semi-supervised CNN-LSTM model), and evaluation metrics (Elbow method, Silhouette score, Davies-Bouldin index for clustering quality, and categorical cross-entropy for classification loss).

Conclusion

This research illustratestheseamlessintegrationof computational rigor,languageanalysis,psychological understanding, and sophisticated methodologies. From the early clustering with K-means and NLP to building a sound hybrid modelintegratingCNNsand RNNs, every phase was carefully designed to reveal the complex patterns of Twitter user behavior. The results highlight the revolutionary potential of multidisciplinary methods in massive-scale social media analysis, opening doors to futurebreakthroughs in understanding and forecastingonline personalities. Future studies can investigatethe seamless fusion of text and image data, crossing platform boundaries to record users\' holistic online trace. Multimodal neural networks can potentially improve prediction accuracy and sentiment analysis. In addition, placing emphasis on privacy-preserving methods and adaptable fusion mechanisms will be crucial for realizing accurate model applicability in various digital environments.

References

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Copyright

Copyright © 2025 Dhruv ., Mr. Jamkhongam Touthang , Dikshant Jajoriya, Harsh Umak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.