Bibliographic record
Abstract
Social media users are growing dramatically every day. In particular, Twitter plays a significant role in easily transmitting information to others. In worldwide 330 million people are using twitter based on the last quarter of 2020 survey. Every second, on an average around 6000 tweets are being tweeted on twitter. Since twitter has a large network base, it is mainly used for connecting people and allows them to share their thoughts. Sentiment analysis uses the processing of natural language, text mining and computer linguistics to collect valuable knowledge for the decision-making process. In this article, we concentrate on a specific post about Shedecides. Shedecides is a movement that aims to preserve the basic rights of women and girl children. It facilitates the women to make decision about their personal life and education by themselves and not depending on others. Any movement supported at the local level do not report the global support unit. So, we collect the tweets from public to analyse the level of support from the society for Shedecides movement. Keywords-Sentiment analysis; Social media; Twitter; Shedecides post.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
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 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".