Aspect Based Sentiment Analysis Framework using Data from Social Media Network

Social media sites are the major source of user generated information on politics, products, ideas and services. Recently social media has become a value able resource for mining sentiment and opinions of public if the data is extracted from it reliably. In this study, a new framework is presented that uses social media network (twitter) stream data as an input and provide output in the form of identified sentiments. The main contribution of this research is a framework that employs data mining and machine learning techniques and analyzes the sentiments by using social network data. Research work has been done on social network website twitter. TF-IDF technique along with Na?ve Bayes performed better (Accuracy 81.24%) in comparison with the other well-known classifiers.