| _version_ | 1866902072419418112 |
|---|---|
| author | Essam Hamed, Lamiaà |
| author_facet | Essam Hamed, Lamiaà |
| contents | <p>Game Development is one of the most important emerging fields in software engineering era. Game addiction is the nowadays disease which is combined with playing computer and videogames. Shame is a negative feeling about self evaluationas well as guilt that is considered as a negative evaluation of the transgressing behaviour, both are associated withadaptive and concealing responses. Sentiment analysis demonstrates a huge progression towards the understanding of web users’ opinions. In this paper, the sentiments of game developers are examined to measure their guilt’s emotions when working in this career. The sentiment analysis model is implementedthrough the following steps: sentiment collector, sentiment pre-processing, and then machine learning methods were used. The model classifies sentiments into guilt or no guilt and is trained with 1000 Reddit website sentiment. Results have shown that Support Vector Machine (SVM) approach is more accurate incomparison to Naïve Bayes (NV) and Decision Tree.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18172676 |
| institution | Zenodo |
| language | |
| publishDate | 2018 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Investigating Game Developers' Guilt Emotions Using Sentiment Analysis Essam Hamed, Lamiaà <p>Game Development is one of the most important emerging fields in software engineering era. Game addiction is the nowadays disease which is combined with playing computer and videogames. Shame is a negative feeling about self evaluationas well as guilt that is considered as a negative evaluation of the transgressing behaviour, both are associated withadaptive and concealing responses. Sentiment analysis demonstrates a huge progression towards the understanding of web users’ opinions. In this paper, the sentiments of game developers are examined to measure their guilt’s emotions when working in this career. The sentiment analysis model is implementedthrough the following steps: sentiment collector, sentiment pre-processing, and then machine learning methods were used. The model classifies sentiments into guilt or no guilt and is trained with 1000 Reddit website sentiment. Results have shown that Support Vector Machine (SVM) approach is more accurate incomparison to Naïve Bayes (NV) and Decision Tree.</p> |
| title | Investigating Game Developers' Guilt Emotions Using Sentiment Analysis |
| url | https://doi.org/10.5281/zenodo.18172676 |