Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi
Abstract
Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it
provides decision-makers with uncensored public views on products, government policies, and programs. Through natural
language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using
traditional statistical learning. The challenge encountered in machine learning method-based sentiment classification still remains
the abundant amount of data available, which makes it difficult to train the learning algorithms in feasible time. This eventually
degrades the classification accuracy of the algorithms. From this assertion, the effect of training data sizes in classification tasks
cannot be overemphasized. This study statistically assessed the performance of Naive Bayes, support vector machine (SVM), and
random forest algorithms on sentiment text classification task. The research also investigated the optimal conditions such as
varying data sizes, trees, and kernel types under which each of the respective algorithms performed best. The study collected
Twitter data from Ghanaian users which contained sentiments about the Ghanaian Government. The data was preprocessed,
manually labeled by the researcher, and then trained using the aforementioned algorithms. These algorithms are three of the most
popular learning algorithms which have had lots of success in diverse fields. 'e Naive Bayes classifier was adjudged the best
algorithm for the task as it outperformed the other two machine learning algorithms with an accuracy of 99%, F1 score of 86.51%,
and Matthews correlation coefficient of 0.9906. The algorithm also performed well with increasing data sizes. 'e Naive Bayes
classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with
Big Data.
Description
Research Article