Analyzing Sentiment thru Natural Language Processing: A Comparison of Machine Learning Models

Abstract

With the help of Natural Language Processing (NLP), sentiment analysis has become much more effective, allowing businesses to glean insights from textual data like reviews, social media posts, and consumer feedback. an analysis of different sentiment analysis machine learning models, including classics like Naive Bayes and Support Vector Machines (SVM) and more recent algorithms like Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformer-based models like BERT. Finding out how well each model does on various datasets allows us to compare and contrast their accuracy, scalability, and processing speed. features, data preparation, and the effects of employing language models that have already been trained. help researchers and practitioners optimize sentiment analysis applications across different industries by shedding light on how to choose the best model for different sentiment analysis jobs.