A Comparative Study of Sentiment Analysis Techniques: Machine Learning vs. Deep Learning

In this paper, we evaluated the performance of four distinct models namely, Logistic Regression, Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM), all trained on the same dataset. Notably, the LSTM model outshone its counterparts, exhibiting remarkable proficiency in figuring out the subtle feelings and moods in the language. Its ability to grasp complex contextual relationships surpassed traditional Machine Learning models like Logistic Regression, Naive Bayes, and Random Forest. This highlights the evolving landscape of sentiment analysis, where the depth and sequential understanding offered by Deep Learning models, especially LSTM, prove invaluable in capturing the intricacies of human expression. These findings prompt a revaluation of the balance between traditional and advanced techniques, emphasizing the importance of leveraging sophisticated models for tasks requiring a nuanced comprehension of sentiment.