A Review of Sentiment Analysis Opinion Mining and Using Machine Learning

Recently, sentiment analysis and opinion mining have drawn a lot of
interest. Because user-generated content on the internet is becoming more and more
influential, this research examines the various machine learning (ML) techniques used
in opinion mining and sentiment analysis applications. The opinion mining and
sentiment analysis utilizing machine learning approaches are thoroughly reviewed in
this research article. The objective of this comprehensive review is to find the most
accurate and efficient models that can automatically classify and analyze sentiments
expressed in textual data. A range of machine learning techniques are utilized and
assessed according to performance measures including precision and accuracy. The
dataset used consists of real-world text data collected from social media platforms,
product reviews, and online forums. The findings indicate that Support Vector Machine
(SVM)and Naïve Bayes (NB) achieved exceptionally high values of accuracy. SVM
and NB achieved an accuracy of 95%. On the other hand, Logistic Regression (LR) and
K-Nearest Neighbor (KNN) demonstrated comparatively lower accuracy scores of 57%
respectively. Among all the evaluated techniques, KNN exhibited the lowest precision
score of 57%. Overall, ML techniques have proven to be valuable in sentiment analysis
and opinion mining.