Bengali Functional Sentence Classification through Machine Learning Approach

In the early time, very few studies were accomplished in Bengali functional sentences. However, the study on Bengali has incredibly increased for its structural diversity. Inspired by those studies, Functional sentence classification in Bengali language was completed including machine learning approaches to classify the sentences. Three types of Bengali functional sentences such as Assertive, Interrogative and Exclamatory have been considered for the research. So the leading purpose of the study is to classify the sentence and find out the best algorithm with comparing accuracy rate. Data have been collected, categorized and processed the dataset properly to avoid the conflict. Some popular machine learning algorithms such as Naive Bayes (NB), Decision Tree Classifier (DT), SVM, KNN, Random Forest (RF), and XGB Classifier have been implemented to compare accuracy rates. Parameters such as Precision, Recall, F1-Score, Support and Confusion matrix have been calculated for the comparison. The comparison demonstrated that performance of the Random Forest, SVC, and XGB Classifier is better than Naive Bayes and Decision Tree Classifier. Remarkable issue is that the Random Forest algorithm provided the highest performance value with an accuracy of 75.38% which is average performance for such a dataset.