Text Classification Using NLP by Comparing LSTM and Machine Learning Method

Natural Language Processing (NLP) has seen significant advancements recently, leading to various applications across different domains. This research focuses on text classification by comparing the performance of Long Short-Term Memory (LSTM) networks with several traditional machine learning algorithms, including Naive Bayes, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. The novelty of this study lies in its comprehensive comparative analysis using a balanced dataset. The data balancing was achieved through the Synthetic Minority Over-sampling Technique (SMOTE), ensuring robust model training. Experimental results reveal that the SVM algorithm achieves the highest accuracy of over 96%, surpassing other models in performance. This indicates that despite the advancements in deep learning, traditional algorithms like SVM remain highly effective for text classification tasks. The findings provide valuable insights into the strengths and weaknesses of different NLP approaches, contributing to the ongoing development of more accurate and efficient text classification models. Future work could explore hybrid models and it application to diverse datasets to further enhance classification accuracy.