A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model

Abstract

There is a growing interest in topic modeling to decipher the valuable information embedded in natural texts. However, there are no studies training an unsupervised model to automatically categorize the social networks (SN) messages according to personality traits. Most of the existing literature relied on the Big 5 framework and psychological reports to recognize the personality of users. Furthermore, collecting datasets for other personality themes is an inherent problem that requires unprecedented time and human efforts, and it is bounded with privacy constraints. Alternatively, this study hypothesized that a small set of seed words is enough to decipher the psycholinguistics states encoded in texts, and the auxiliary knowledge could synergize the unsupervised model to categorize the messages according to human traits. Therefore, this study devised a dataless model called Seed-guided Latent Dirichlet Allocation (SLDA) to categorize the SN messages according to the PEN model that comprised Psychoticism, Extraversion, and Neuroticism traits. The intrinsic evaluations were conducted to determine the performance and disclose the nature of texts generated by SLDA, especially in the context of Psychoticism. The extrinsic evaluations were conducted using several machine learning classifiers to posit how well the topic model has identified latent semantic structure that persists over time in the training documents. The findings have shown that SLDA outperformed other models by attaining a coherence score up to 0.78, whereas the machine learning classifiers can achieve precision up to 0.993. We also will be shared the corpus generated by SLDA for further empirical studies.

The role of Passion and Self-Efficacy in entrepreneurial activities in the gig economy: An Unsupervised Machine Learning Analysis with Topic Modeling

“…These findings collectively validate the selection of six topics, striking a balance between quantitative metrics and meaningful topic interpretation in alignment with research objectives and dataset characteristics. The (k) value based on the outcome of the Gibbs sampling procedure was used to access the data, as shown in Figure 6, using LDA to capture substantial inter-intra structures of the data (Sagadevan et al, 2022). As mentioned above, the seed number of 1961 was also set to the LDA to ensure reproducibility.…”

Special Issue on Ensemble Learning and/or Explainability

“…The sixth paper is entitled "A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model" and it is authored by Sagadevan et al [9]. In this research, the authors proposed a new unsupervised model called SLDA (Seed-guided Latent Dirichlet Allocation) to classify social network (SN) messages according to personality (Psychoticism, Extraversion, and Neuroticism-PEN) traits.…”

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“…In the classification method there are several phases of completion, starting from training data and ending with the data testing process so that an accurate decision is produced. The following is a picture of the solution flow of the Classification method [23].…”