A practical application for sentiment analysis on social media textual data
Bibliographic record
Abstract
With the amount of data that is available today in textual form, it is essential to be able to extract as much useful information as possible from them. While some textual documents are easy to be understood, other textual documents may need extra processes to discover the hidden information within it. For instance, how the author was feeling while writing this piece of text, or what emotions authors are expressing in this piece of text. The idea of discovering what emotions are expressed in a textual document is known as sentiment analysis. The interest in sentiment analysis has been steadily growing in the past decade. Being able to accurately detect and measure the different emotions present in a text has become more and more useful as the availability of online resources has increased. These resources can range from product reviews to social media content. Each of these resources presents their own distinct challenges while still sharing the core techniques and procedures. In this paper, we introduce an application that can detect four distinct emotions from social media posts. We will first outline the techniques we have used as well as our outcomes, then discuss the challenges that we faced, and finally, our proposed solutions for the continuation of this project.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".