Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach
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,...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2021/5561204 |
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| author | John Andoh Louis Asiedu Anani Lotsi Charlotte Chapman-Wardy |
| author_facet | John Andoh Louis Asiedu Anani Lotsi Charlotte Chapman-Wardy |
| author_sort | John Andoh |
| collection | DOAJ |
| description | 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. The 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. The Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data. |
| format | Article |
| id | doaj-art-acb00f3c36844b259b7bcf506b4adc9c |
| institution | Kabale University |
| issn | 1687-5893 1687-5907 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-acb00f3c36844b259b7bcf506b4adc9c2025-02-03T06:47:02ZengWileyAdvances in Human-Computer Interaction1687-58931687-59072021-01-01202110.1155/2021/55612045561204Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning ApproachJohn Andoh0Louis Asiedu1Anani Lotsi2Charlotte Chapman-Wardy3Department of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaDepartment of Statistics & Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, GhanaGathering 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. The 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. The Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data.http://dx.doi.org/10.1155/2021/5561204 |
| spellingShingle | John Andoh Louis Asiedu Anani Lotsi Charlotte Chapman-Wardy Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach Advances in Human-Computer Interaction |
| title | Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach |
| title_full | Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach |
| title_fullStr | Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach |
| title_full_unstemmed | Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach |
| title_short | Statistical Analysis of Public Sentiment on the Ghanaian Government: A Machine Learning Approach |
| title_sort | statistical analysis of public sentiment on the ghanaian government a machine learning approach |
| url | http://dx.doi.org/10.1155/2021/5561204 |
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