Entropy Analysis of a Dataset using Machine Learning Approach

Abstract

Artificial Intelligence and Machine Learning's component to recognise communication between machines and individual (real) languages is a natural language processing. Natural Language Processing is the identification of emotion mostly used to interpret terms to provide strong, slightly bad emotions due to people's reading patterns. Shannon's entropy helps me know whether or not people more like Zomato is a ranking program for restaurants. The assessment involves a restaurant review that can be used for entropy assessment. On this basis, the authors want to respond to the expected view of the analysis. The method used to pre-process the research is to minimise all terms, monitor access, remove quantities, sentence structure, stop words and compile. The latent semantic document frequency (TF-IDF) is then constructed from word to vector. The data we are gathering is 1,50,000 reviews. Great responses are rated 3 and above, poor comments are rated 3 and below, glowing reviews are rated 3 and above. The author uses split Evaluation, 80% full and 20% Data Screening. Accuracy, recall and precision is the criteria used to evaluate random forest classifiers. The reliability of this analysis is 92 percent. 92 %, 93 %, 96 % is the consistency of each selection's thoughts and feelings. 99%, 89%, 73% are a reminder of positive, pessimistic and constructive views. 93 % and 87 % are the average accuracy and recall. "Poor", "great", "fair", "better", "location", "care", "request", "food", "seek" and "pleasant" are the 10 terms that influence the results.