Sentiment Analysis Using Machine Learning

Abstract

In the era of electronic correspondence, enormous volumes oftextual data are generated daily through platforms such as social media, reviews, and forums. Extracting meaningful insights from this unstructured data has become increasingly important for businesses, governments, and researchers. Sentiment analysis, often known as opinion mining, is a natural language processing (NLP) technique that evaluates the emotional tone of a document. A method to sentiment analysis with machine learning techniques is presented in this research. In order to categorize text into positive, negative, or neutral attitudes, the study investigates the use of many supervised learning techniques, such as Naive Bayes, Support Vector Machines (SVM), and Logistic Regression. Benchmark datasets like Twitter data and IMDb movie reviews are used to train and assess the algorithm. To improve model performance, preprocessing techniques such as vectorization (TF-IDF), tokenization, and stop-word deletion are used. The findings suggest that machine learning models, with SVM exhibiting especially good performance, can classify sentiment with high accuracy. This study demonstrates how machine learning can be used to better understand public sentiment and assist in decision-making in a variety of industries.