| dc.contributor.author | Atwine, Nickson | |
| dc.contributor.author | Mugabo, Amuza | |
| dc.contributor.author | Turyahiirwa, Happiness | |
| dc.contributor.author | Mulidwa, Henry | |
| dc.date.accessioned | 2020-01-10T13:33:57Z | |
| dc.date.available | 2020-01-10T13:33:57Z | |
| dc.date.issued | 2019-05-05 | |
| dc.identifier.citation | Atwine, Nickson Mugabo, Amuza Turyahiirwa, Happiness and Mulidwa, Henry (2019). Social media posts classification system. Undergraduate project report. Makerere University | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/8349 | |
| dc.description | Project report submitted to the School of Computing and Informatics Technology for the study leading to a project in partial fulfilment of the requirements for the award of the degree of bachelor of science in software engineering of Makerere University | en_US |
| dc.description.abstract | This report demonstrates the production of a sentiment analysis system, with the following main objectives set:
1. Implement a machine learning algorithm to perform sentiment analysis.
2. Understand and implement natural language processing technique.
3. Achieve a classification accuracy of over 75%.
4. Build a graphical user interface to enhance the interaction with the users of the system as well as manage users of the system and also for visualization purpose.
In order to produce the software artefacts presented in the report, computer science knowledge, as well as machine learning and natural language processing techniques were employed. Consequently, the concepts and techniques, which contributed to the development of the project, such as the Naive Bayes algorithm, logistic regression, are explained.
Furthermore, a high-level view and a low-level view of the system produced are detailed in subsequent chapters. The quality is assessed in the Achievements section, where a performance benchmark and other evaluation techniques are employed. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | natural language processing technique | en_US |
| dc.title | Social media posts classification system | en_US |
| dc.type | Technical Report | en_US |
