Political Analytics on Election Candidates and Their Parties in Context of the US Presidential Elections 2020
Bibliographic record
Abstract
The availability of internet services in the United States and rest of the world in general in the \nmodern past has contributed to more traction in the social network platforms like Facebook, \nTwitter, Instagram, YouTube, and much more. This has made it possible for individuals to freely \nspeak and express their sentiments and emotions towards the society. Social media has also made \nit possible for bringing people closer by making the world a global village. There are influencers \nwho promote products on social media platforms and politicians run their campaigns online for \nbroader reach. Social media has become the fuel for globalization. In 2020, the United State \nPresidential Elections saw around 1.5 million tweets on Twitter specifically for the Democratic \nand Republican party, Joe Biden, and Donald Trump, respectively. The tweets involve people’s \nsentiments and opinions towards the two political leaders (Joe Biden and Donald Trump) and \ntheir parties. The computational study of beliefs, sentiments, evaluations, perceptions, views, and \nfeelings conveyed in text is known as sentiment analysis. The political parties have used this \ntechnique to run their campaigns and understand the opinions of the public. It has also enabled \nthe modification of their campaigns accordingly. In this thesis, during the voting time for the \nUnited States Elections in 2020, we conducted text mining on approximately 1.5 million tweets \nreceived between 15th October and 8th November that address the two mainstream political \nparties in the United States. We aimed at how Twitter users perceived for both political parties \nand their candidates in the United States (Democratic Party and Republican Party) using VADER \n(Valence Aware Dictionary and sEntiment Reasoner) a sentiment analysis tool that is tailored to \ndiscover the social media emotions, with a lexicon and rule-based sentiment analysis. The results \nof the research were the Democratic Party’s Joe Biden regardless of the sentiments and opinions \nin the in Twitter showing Donald Trump could win.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".