Sentiment Analysis of Social Media Network Using Random Forest Algorithm

Sentiment Analysis is the identification of sentiments or opinions from the given text. Social media generates large amount of sentiment loaded information in the form of reviews. Sentiment analysis is used to identify the customer's opinions from user reviews. Online purchasing have became more fashionable due to its varieties, low cost and immediate supply. In today's competitive ecommerce market ratings and reviews of various brand is used to understand how consumers really feel about the product. The feedback environment is developed to help the customers to buy the correct product and to guide the companies to enhance the features of product depending on the consumer's demand. The customer feels difficult to accurately find the review for a particular feature of a product that they intend to buy. Also, there is a mixture of positive and negative reviews. To avoid this confusion and to make this review system more transparent and user friendly, feature based opinion extraction is carried out. In this paper the rating from the online shopping website known as flipkart.com is analyzed, based on the aspects of the product the rating is classified as positive, neutral and negative. The proposed work is analyzed by using Machine Learning algorithm called Random Forest and simulated by using SPYDER. In our system the accuracy, precision, F-measure and recall is calculated for both Random Forest and Support Vector Machine (SVM) algorithm and then accuracy comparison is made these two algorithms. In which the Random Forest gives the best accuracy of 97% than the Support Vector Machine.