Analysis of Peoples’ Opinion on Democratic Election in A Developing Nation Using Textblob Lexicon-Based Approach
Bibliographic record
Abstract
The field of Sentiment Analysis involves utilizing computational methods to identify and understand the emotional aspects present in a text and expressing emotions through written language.Hence, the objective of this research is to examine and analyze people's opinions regarding the Nigerian presidential election for a deeper understanding of their preferences, and concerns of the electorate on the person declared as the winner of the election.Using the Tweepy library and Twitter Application Programming Interface (API), 85,662 tweets were collected using some specific keywords and hashtags.The tweets were preprocessed using Natural Language Tool-kits and were analyzed using TextBlob lexicon-based processing techniques.The results showed that the Positive attitudes were 48%, neutral attitudes were 32%, and negative attitudes were 23%.Insights into popular sentiment and political inclinations that were gained from the sentiment analysis showed that most individuals had a positive reaction toward the presidential election for the betterment of their nation.In general, this research shows the importance and effect of sentiment analysis in determining people's opinions towards the conducted election.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".