In machine learning, sentiment analysis is the way to analyze data in text format. This study has proposed a machine-learning approach to predict the polarity of customer reviews.
This was a part of my undergraduate course project, CSE 445: MACHINE LEARNING. Here, We have utilized machine learning approach to predict user sentiments from Bangla texts about products available on e-commerce sites. In order to accomplish the task, we have constructed a Bengali corpus of the public views about products and services of multiple Bangladeshi E-commerce organizations. Besides, we have applied six different machine learning algorithms (Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Stochastic Gradient Descent(SGD)) to predict and analyze the the polarity of public sentiments. Term Frequency–Inverse The document Frequency (TF-IDF) technique has been applied by using Trigram features. Finally, after optimizing the hyperparameters using the Randomized- SearchCV algorithm, SVM classifier has been found to demonstrate the highest accuracy of 90.68% for predicting public sentiments.
Paper Available Online: https://ieeexplore.ieee.org/abstract/document/10103350