Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach
Keywords:Sentiment analysis, Naïve Bayes, Education, Students, Polarity, Twitter
The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naïve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naïve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naïve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education.
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