Supervised Machine Learning Entity Sentiment Analysis: Prediction of Support for 2024 Indonesian Presidential Candidates
Bibliographic record
Abstract
is a political year in Indonesia as it marks the presidential general election.The proliferation of survey institutions attempting to capture the electability levels of each candidate may not invariably yield accurate results, as evidenced by the events of the 2016 United States Presidential election.The loyal support creates tight competition and a narrow margin in electability levels among the three contending candidates.Opinion mining on social media offers an alternative that addresses the challenges often encountered when measuring electability using traditional survey methods.This study aims to build entitylevel sentiment classifiers as a new approach for predicting electability of presidential candidates based on citizen support on social media Twitter within the framework of the CRISP-DM model.The study compares 9 different algorithms with 3 vectorization techniques.Evaluation measurement with 4 metrics: accuracy, precision, recall and f1score is performed.As a result, TF-IDF 3-gram Random Forest achieves the highest fiscore 0.84486.The selected model is then employed to measure the presidential candidates' electability levels over time.Besides streamlining the process, social media's opinion mining enables the candidates and their constituents to monitor electability levels affordably in real-time and on-demand manner, which is advantageous compared to traditional surveys.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".