Assessing Institutional Performance Using Machine Learning Algorithms
DOI:
https://doi.org/10.31185/wjcms.263Keywords:
Sentiment analysis, Social media, Machine Learning Algorithms, TF-IDFAbstract
In Middle Eastern nations, social media has grown in importance in influencing political and governmental choices. In Iraq, Facebook is regarded as one of the most widely used social networking sites. The underutilization of this tool for evaluating institutional performance persists. Thus, using sentiment analysis on Facebook, this study suggests a methodology that aids organizations like the Ministry of Justice in Iraq in assessing their own performance. The model makes use of a variety of machine learning methods, including Naive Bayes, Logistic Regression, and Support Vector Machine. TF-IDF (Term Frequency-Inverse Document Frequency) was used to convert the textual data into numerical features, which is essential for effective text analysis. Additionally, features were carefully managed by utilizing both unigram and bigram models. Using datasets from (Facebook pages belonging to the Iraqi Ministry of Justice), a thorough experimental investigation was conducted. The results of our experiments showed that the SVM algorithm produced the best accuracy, at 98.311%. following the suggested model's retention of a few stop words, which was shown to significantly improve the algorithm's performance and guarantee accurate categorization of comments while maintaining the content of the phrase.
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