Deep Neural Network Approach for Sentiment Analysis of Tweets Related to Sports Concussion
Bibliographic record
Abstract
Concussion and Traumatic brain injuries (TBI) are serious injuries that impair the normal functionality of a person’s brain. Symptoms can include confusion, disorientation, loss of consciousness, memory lost, and in more sever situations fatality. It is reported that 39% of children (ages 10-18 years old) who visit the hospital due to a sports-related head injury were diagnosed with concussion and 24% with the possible concussion [1]. In order to bring awareness about the seriousness of the TBI to the attention of the policy makers, a neural network based sentiment analysis ensemble system that automates the process of gathering the opinion of the general public is designed. A preprocessing pipeline is proposed that embeds various word-level features into a single concatenated vector. Input vectors are processed by varying Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Networks. The proposed ensemble system achieves an evaluation score of 62.71% based on its precision and recall, and compares well with other state-of-the art systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".