In this study, press freedom in Nigeria is investigated, together with its recent developments and historical background. It looks at the state of press freedom, highlighting obstacles, worldwide forces that contribute to its restriction and the benefits it provides. Despite legislative restrictions, government control over Nigeria's media has persisted since the Newspaper Ordinance of 1903. The study places press freedom within several theoretical frameworks, such as authoritarian, libertarian, and democratic participant theories, using McQuail's theories of mass communication. Using Google Forms (...) and the River sampling method, the study obtained data from 60 Nigerian journalists with a 100% response rate. The study underscores the critical function of press freedom in democratic nations, calls for its protection, and emphasizes its ability to influence open government, the defense of human rights, and socioeconomic progress. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of (...) 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)