Enhanced Sentiment Classification Using Geo Location Tweets

Information shared by millions of people in social networks. It is an important way to know the views on political issues, the vision of social problems, the latest trends, commercial websites, etc. In particular, Twitter is an important source of knowledge. It is useful to perform a moral assessment of the service. The classification of emotions on tweets has given a financial and effective form to determine public sentiment. In this research, more than 30,000 location-based tweets are collected from the Twitter page also emotions (emoji‘s) and the abbreviated form of words are used for the sentiment classification by replacing in top form, then perform the opinion classification in 5 categories, extreme positive, positive, neutral, negative and extreme negative classes. Suppliers and shareholders help to take into account the opinions of modes, services, transactions, etc from a particular geographical location. It also performs a logical analysis of many products or services on the same tool.