Analysis of Serious Games for Nutrition Using NLP Techniques
Bibliographic record
Abstract
Serious games, which are games that are designed not just for entertainment, are commonly used to teach important concepts such as a new language, or other specific subjects such as healthy nutrition. Serious games can be a fun and interactive way to educate people about healthy eating habits which can go a long way to address health challenges such as obesity. These games are typically available to users on the app store such as the Google Play Store where users can also write reviews based on their experience playing the game. The reviews are a rich source of information and feedback for game developers and other stakeholders. Analyzing the sentiments and emotions expressed in such reviews can help developers gain insights into how players perceive a game’s effectiveness in achieving its goals and aims. This can aid the development of future releases of the game where issues are fixed and improvements made. To contribute to research in this area, ten games are analyzed for sentiments and emotions using Natural Language Processing techniques. Our results show that all but one of the games have more positive sentiments than negative sentiments. In addition, some of the popular emotions we identified include admiration, approval, amusement, and love. To support game designers and other stakeholders in gaining useful insights from our results, we developed a tool in the form of an interactive web page where users can view the sentiments and emotions expressed in the reviews of each game including the raw reviews. This can help game designers and other stakeholders explore reviews of users in one place.
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 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".