Hate Speech Detection Framework from Social Media Content the Case of Afaan Oromoo Language
| dc.contributor.advisor | Gizaw, Solomon (PhD) | |
| dc.contributor.author | Guta, Lata | |
| dc.date.accessioned | 2021-12-15T07:28:40Z | |
| dc.date.accessioned | 2023-11-29T04:06:32Z | |
| dc.date.available | 2021-12-15T07:28:40Z | |
| dc.date.available | 2023-11-29T04:06:32Z | |
| dc.date.issued | 2021-12-02 | |
| dc.description.abstract | Hate Speech on social media has unfortunately become a common occurrence in the Ethiopia online community largely due to advances in mobile computing and the Internet. The connectivity and availability of social media platforms in the world allow people to Interact and interchange experiences easily. However, the anonymity and flexibility afforded by the Internet have made it easy for users to communicate aggressively. Hate Speech affects the society in many aspects, such as affecting the mental health of targeted audiences, affects social interaction, leads to violence and distraction of properties. Identifying a text that containing Hate Speech regularly is difficult task for humans, it is tedious and time consuming. To solve the newly emerged Hate Speech propagation in social media sites, recent studies employed different Machine learning algorithms and feature engineering techniques to detect Hate Speech messages automatically. In case of Afaan Oromoo language there is a work on Sentiment Analysis of Afaan Oromo using Machine learning Approach.but it is not in case of Hate and neutral classification rather oponions. In this research, a new Afaan Oromoo Hate Speech dataset from Facebook social media that are labeled into binary classes. TF-IDF, N-gram and word2ve feature are used as a feature for the Machine learning models. We evaluate the models using 80% for training and 20% for testing purpose by using train-test split with accuracy, precession, recall, and f1-score performance metrics were used to compare the models. The model based on LSVM with TF-IDF combination with N-gram achieves slightly better performance than the other models. Support Vector Machine(SVM) algorithm achieve the highest accuracy of 96% which is promised result. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/29314 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Hate Speech | en_US |
| dc.subject | Detection Framework | en_US |
| dc.subject | Social Media Content | en_US |
| dc.subject | Case of Afaan Oromoo Language | en_US |
| dc.title | Hate Speech Detection Framework from Social Media Content the Case of Afaan Oromoo Language | en_US |
| dc.type | Thesis | en_US |