Personality Classification Based on Textual Data using Indonesian Pre-Trained Language Model and Ensemble Majority Voting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Personality is a collection of striking traits and behaviors of a person.The use of personality models can be applied in employee recruitment systems or to analyze characteristics and potential in more depth.Personality models are usually made using psychological test data or filling out questionnaires.However, this requires a long time.Building a personality classification model using NLP and deep learning is considered one of the best solutions.However, the performance of the classification model still needs to be improved, especially for Indonesian Language data.So, this research makes a personality classification model with Indonesian Language data using BERT-based architectures such as Multilingual BERT, IndoBERT, and Indonesian RoBERTa Base with an ensemble majority voting technique.Data limitations and imbalances were addressed using synonym replacement by incorporating words from a pre-trained model, MBERT.Information contained in social media often has ambiguous meanings because the words conveyed are not standardized, so this study tries to retain the information contained in the text by translating emoticons and slang words at the preprocessing stage to help keep the meaning of words in context.The proposed approach's research results can improve the classification model's results in classifying personality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it