CLASSIFICATION OF USER OPINIONS FROM TWEETS USING MACHINE LEARNING TECHNIQUES

Abstract

Online Social Network is a standard platform for collaboration, communication where people are connected to each other for sharing their opinion. In general, opinions can be articulated about anything like products, surveys, topics, individuals, organizations and events. There are two main types of textual information in web like facts and opinions. Facts can be expressed in defined terms by the user implicitly. To mine opinion, from the user defined facts is intellectually very demanding. User opinion is valuable data, which can be used for marketing research in business during decision making process. So opinion mining and classification plays a vital role in predicting what people think about products. In this work, basic Natural Language Processing (NLP) techniques and hash tag segments, emoticons are used for classification. The performance comparison of Support Vector Machine (SVM), Naïve bayes (NB) and Multilayer Perceptron (MLP) are done using weka. It is observed that the MLP gives better accuracy to classify the opinion from tweets