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)

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)

This study investigated the consumption pattern of wild edible green leafy vegetables found in Osogbo Local Government Area of Osun State using a descriptive survey research design. The sample size for this study was two hundred and eleven (211) respondents. A four-likert scale structured questionnaire containing twenty-four (24) items was used for data collection. Data were analysed using mean scores and standard deviation. The findings of the study revealed that ”Yarin,” “Worowo,” “Gbure,” and “Ebolo" are major wild edible green leafy (...) vegetables found in the study area, and their consumption is quite low. The finding showed that wild edible green leafy vegetables have benefits such as alleviating malnutrition at no cost and can be a good source of income. The findings also showed that urbanisation's preference for cultivated vegetables over wild edible green leafy vegetables, as well as uncultured ideas like ignorance and poverty and well-researched information about cultivated leafy vegetables, are among the factors influencing the intake of wild edible green leafy vegetables. The findings of this study asserted that wild edible green leafy vegetables, when compared with cultivated ones, can alleviate malnutrition, prevent micronutrient deficiency, and equally serve as a source of income at little or no cost. Therefore, it was recommended that there is a need to explore, analyse, and document that wild green edible leafy vegetables are nutritious, health-promoting, and easy to cultivate, and bring out substantial yields even under difficult climatic and edaphic conditions. (shrink)

The significance of hair is deeply rooted in African ontology. It depicts leadership status and when shaved off completely, may sometimes signify mourning or lack of dignity. In Benin-city of Edo state in Nigeria, Chiefs who are mostly men are identified by their unique hair-styles. It shows their position of leadership in the society and when a king dies, all the men in the kingdom are expected to shave off their hair as a sign of respect for the departed king. (...) In the culture of the Yoruba people of Nigeria, hair is highly celebrated by the women as it is seen as a crown of glory upon their heads. With advancement in globalisation and cross-cultural relationships, the culture of hair and its ontological significances are gradually being eroded as expressed in Chimamanda Adichie‟s „Americanah‟. For this reason, the hair, being a very important symbol of cultural identity has become a key role in understanding the issues with identity crises in Africa. The paper analyses the ontological significances of hair in Africa, evaluates the issues involved in the misrepresentation of African hair as presented in some African literature as well as interrogates the issues of identity crises occasioned by African hair. (shrink)

This paper interrogates Abdul Rasheed Abiodun Adeoye’s ‘neo-alienation’ directorial style’ as a viable directing technique on African theatre stage. The ‘neo-alienation’ style is an offshoot of Bertolt Brecht’s epic theatre that enhances the total theatre aesthetics and encourages actors’-audience’s relationship. It is a directorial style that has been tested severally on the Nigerian stage and proved worthy of exploration. Using the participant-observation and the deductive research methods, the theory shall be examined in relation to its usage in the production (...) of Adeoye’s The Smart Game- (A Neo- Alienation Script) and Wole Soyinka’s The Lion and the Jewel as directed by AbdulRasheed Abiodun Adeoye in 2002 and 2004 respectively. Thus, the study aims to archive and document the ‘neo alienation’ directing theory because of its immense aesthetics values to African plays and theatre directors. The study also showcases Adeoye as a conscientious theatre director, in whose works, other directors can learn from. The study concludes that the ‘neo-alienation’ aesthetics has been found to be a unique style full of theatrical spectacles that are capable of adding aesthetics and enhance African productions if explored. It is, therefore, recommended that Nigerian and African directors should work towards propounding at least a directorial style each which will add value to African theatre; the study also suggests that directors who have evolved their own directorial styles should endeavor to have them documented for posterity. (shrink)

In this article I critically discuss Professor Oladele Abiodun Balogun’s reflections on the proper final ends of doing philosophy and related sorts of abstract, speculative, or theoretical inquiry. Professor Balogun appears to argue that one should undertake philosophical studies only insofar as they are likely to make a practical difference to people’s lives, particularly by contributing to politico-economic development, or, in other words, that one should eschew seeking knowledge for its own sake. However, there is one line of thought (...) from Professor Balogun, about philosophy being able to make life meaningful, that I argue ultimately––perhaps contrary to his intentions––entails that it can be appropriate to some degree to pursue philosophy that is unlikely to ameliorate poverty and similar social ills. My central aims in this article are to identify Professor Balogun’s strongest argument against pursuing any knowledge for its own sake and to argue that an appeal to meaningfulness constitutes a strong, competing reason to seek out some of it. (shrink)