Performance Evaluation of Machine Learning Classification Methods for Sentiment Analysis
Neha Singh, Umesh Chandra Jaiswal, Jyoti Prakash Srivastava · 2023
The use of Natural Language Processing and text processing along with identification and research of sentiments and emotional states from a specific set of data sets are all part of the burgeoning field of sentiment analysis. Many companies use sentiment analysis for product reviews and social media comments to assess whether content is positive, negative, or neutral. This paper investigates classifiers based on various factors and focuses on the creation of many machine learning classification algorithms as well as their performance analysis. The Weka tool will be used to illustrate sentiment analysis utilising Nave Bayes, Multilayer Perceptron, J48, Random Forest, and OneR classifiers, which are machine learning techniques. All classifiers are tested using a 10-fold cross-validation approach. When the outcomes of the five different classifiers were analysed, the J48 classifier had the best prediction accuracy of 98.59 percent.