Sentiment analysis of political discussion on Twitter in Nigeria's 2019 presidential election
Bibliographic record
Abstract
Social media have helped in political communication by reinforcing social change and commitments from governments. This study analyses political discussion on Twitter during the 2019 Nigeria general election. The aim is to identify popular issues discussed and opinion of citizens on popular presidential candidates' social media network and correlate the online discussion with real world events during the 2019 election. Five most popular presidential candidates were selected for the investigation. Latent Dirichlet allocation (LDA) model was used to identify frequent terms and topics discussed. Sentiment of the tweets was analysed using National Research Council Canada (NRC) Emotion Lexicon. Findings revealed that Twitter is mainly used for political discussion that cantered on personal representation, political party's promotion and elections matters. The behaviour of users shows that Twitter platform is not being used to share original political ideologies, instead the platform was used for re-tweeting of tweets and political campaigns for candidates.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".