E-ISSN: 2476-9606 Abstract Proceedings International Scholars Conference Volume 7 Issue 1, October 2019, pp. 1831-1840 https://doi.org/10.35974/isc.v7i1.1003 1831 Sentiment Analysis of Customer Reviews in Zomato Bangalore Restaurants Using Random Forest Classifier Bern Jonathan 1 , Jay Idoan Sihotang 2 , Stanley Martin 3 1 Departement of Technology, Female Daily Network 2,3 Department of Information Technology, Universitas Advent Indonesia bern@femaledaily.com ABSTRACT Natural Language Processing is one part of Artificial Intelligence and Machine Learning to make an understanding of the interactions between computers and human (natural) languages. Sentiment analysis is one part of Natural Language Processing, that often used to analyze words based on the patterns of people in writing to find positive, negative, or neutral sentiments. Sentiment analysis is useful for knowing how users like something or not. Zomato is an application for rating restaurants. The rating has a review of the restaurant which can be used for sentiment analysis. Based on this, writers want to discuss the sentiment of the review to be predicted. The method used for preprocessing the review is to make all words lowercase, tokenization, remove numbers and punctuation, stop words, and lemmatization. Then after that, we create word to vector with the term frequency-inverse document frequency (TF-IDF). The data that we process are 150,000 reviews. After that make positive with reviews that have a rating of 3 and above, negative with reviews that have a rating of 3 and below, and neutral who have a rating of 3. The author uses Split Test, 80% Data Training and 20% Data Testing. The metrics used to determine random forest classifiers are precision, recall, and accuracy. The accuracy of this research is 92%. The precision of positive, negative, and neutral sentiment are 92%, 93%, 96%. The recall of positive, negative, and neutral sentiment are 99%, 89%, 73%. Average precision and recall are 93% and 87%. The 10 words that affect the results are: “bad”, “good”, “average”, “best”, “place”, “love”, “order”, “food”, “try”, and “nice”. Keywords: Sentiment Analysis, Random Forest, Precision-Recall, Feature Selection INTRODUCTION Sharing on the internet is something we usually do. Giving a review is also a useful activity so that other people on the internet can find out something else and see opinions about things. The usual things reviewed by someone in the form of experiences, places, objects, and others. Give a review we usually use text to explain something that we experience with an item, place, or event that we normally experience.