International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 734 Survey of Classification of Business Reviews Using Sentiment Analysis Shilpa A. Shendre 1 , Prof. Pramila M Chawan 2 1 M.Tech Student, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India 2 Associate Professor, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- ABSTRACT:- The rapid increase in mountains of unstructured textual data accompanied by the proliferation of tools to analyze them has opened up great opportunities and challenges for text research. The research area of sentiment analysis has gained popularity in the last years. Business developers not only want to know about there product marketing and profit based on the number of sales been done but also want to know about the reviews and thoughts of people using these products. The feedback they receive via social media and other internet services becomes very important to measure the quality of a product they are serving. Sentiment analysis is a domain where the analysis is focused on the extraction of feedback and opinions of the users towards a particular topic from a structured, or unstructured textual data. In this paper, we try to focus our effort on sentiment analysis on Yelp challenge database. We examine the sentiment expression to classify the reviews of the business whether it is positive or negative and perform the feature extraction and use these features for updating and maintenance of the business. Key Words: sentiment analysis; opinion mining; classification; text reviews, Machine learning 1. INTRODUCTION Sentiment analysis has become an important research area for understanding people’s opinion on a matter by differentiating a huge amount of information. The present era of the Internet has become a huge Cyber Database which hosts the gigantic amount of data which is created and consumed by the users. People across the world share their views about various services or products using social networking sites, blogs or popular reviews sites. The Internet is been growing at an exponential rate giving rise to communicate across the globe in which people express their views on social media such as Facebook, Twitter, Rotten Tomatoes and Foursquare. Opinions which are being expressed in the form of reviews provide a platform for new explorations to find collective reviews of people. One such domain of reviews is the domain of business reviews which affects business people. The feedback from the customer is valuable for companies to analyze their customer’s satisfaction and survey the competitors. This is also useful for other people or consumers who want to buy a product or a service prior to making a purchase. In this paper, we are going to present the results of machine algorithms for classifying reviews using semantic analysis. A large number of customer-generated reviews for businesses and service providers are classified as either positive or negative. We propose a method to automatically classify customer sentiments using only business text review. This helps us to generate the result using feedback without manual intervention. By studying only rating, it is very difficult to judge why the user has rated the product as 1 or 5 stars. However, the text content contains a more quantitative value for analyzing more than rating itself. In this paper, we are going to mention the preprocessing steps require in order to achieve accuracy in the classification task. There is no previous research available on classifying sentiment of business review using the latest reviews forms yelp dataset. Determining the underlying sentiment of business review is a difficult task taking into account several factors such as the connotation of a word depending on the context, language used, words ambiguity when using words that don’t express a particular sentiment or when using sarcasm. We show that a sentiment analysis algorithm built on top of machine learning algorithms such as Naïve Bayes and Linear Support Vector Classification (SVC) has accuracy above 90% business reviews. 1.1 Feature Selection Mostly the researchers apply standard feature selection in there approach to improve performance with few using more practical approaches. We are focusing completely on feature election to improve sentiment analysis are few. One of them is the famous Pang & Lee, who removed objective sentences on a tested consisting of objective and subjective text trained on SVM. Initially, they found that sentiment classification result is actually slow and moderate. They then concluded it was more likely that sentences adjacent to discarded sentences improved classification result over their baseline. 1.2 Information Gain Another work used sophisticated feature selection and found that using either information gain (IG) or genetic algorithm (GA) results in an improvement inaccuracy. Let D be a dataset of labeled texts. Let pD represent the probability that a random text D is classified as positive. The classification should be fairly simple if the text is majorly biased towards positive or negative instances. On the contrary, if the set is very unevenly distributed with equal likelihood of positive and negative instances, then the task is difficult. The disorder in the set D is calculated by its entropy: