Social Media and Sentiment Analysis: The Nigeria Presidential Election 2019
Bibliographic record
Abstract
Social media has become an inevitable tool in many sectors including politics. On February 23, Africa's largest economy and most populous country, Nigeria, conducts its presidential elections. Many Nigerians used the social media to express their opinion in favour or against the various presidential candidates. Research has shown that their shared sentiments can influence the opinions of others and hence who eventually wins the presidential election. This paper therefore aims to identify and analyze public sentiments towards two popular candidates with the aim of determining their chances of being elected into the highest position of authority in Nigeria based on social media comments. First, we perform sentiment analysis on election-related posts from Nairaland (a social network targeted at Nigerians) using lexicon-based and supervised machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e. negative or positive). We collected 118,421 posts between January 1 and February 22, 2019. Second, we implemented and compared the performance of three lexicon-based classifiers and five ML-based classifiers. The best performing classifier is then used in determining the sentiment polarity of posts. Third, we conducted thematic analysis on both positive and negative posts to further understand and reveal public opinions about each candidate. Finally, we discuss our analytical findings and the possibility of a candidate receiving more votes than the other. Our findings relate considerably to the actual election results released by the Independent National Electoral Commission (INEC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".