Performance evolution for sentiment classification using machine learning algorithm

Abstract

Machine Learning (ML) is an Artificial Intelligence (AI) approach that allows systems to adapt to their environment based on past experiences. Machine Learning (ML) and Natural Language Processing (NLP) techniques are commonly used in sentiment analysis and Information Retrieval Techniques (IRT). This study supports the use of ML approaches, such as K-Means, to produce accurate outcomes in clustering and classification approaches. The main objective of this research is to explore the methods for sentiment classification and Information Retrieval Techniques (IRT). So, a combination of different machine learning algorithms is used with a dataset from amazon unlocked mobile reviews and telecom tweets to achieve better accuracy as it is crucial to consider the previous predictions related to sentiment classification and IRT. The datasets consist of user reviews ratings and algorithms utilized consist of K-Means Clustering algorithm, Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) algorithms. The amalgamation of each algorithm with the K-Means resulted in high levels of accuracy. Specifically, the K-Means combined with Logistic Regression (LR) yielded an accuracy rate of 99.98%. Similarly, the K-Means integrated with Random Forest (RF) resulted in an accuracy of 99.906%. Lastly, when the K-Means was merged with the Decision Tree (DT) Algorithm, the accuracy obtained was 99.83%.We exhibited that we could foresee efficient, effective, and accurate outcomes.

Keywords

• machine learning
• k-means
• logistic regression
• random forest
• decision tree algorithms