GES: Group expert system using sentiment analysis on twitter in PHP

With the advent and promulgation of the Internet and e-commerce, it is evident that the complexity of finding relevant information on the Web has become increasingly intricate and crucial. In fact, "information overload" on the Web is a well recognised problem, where users find it increasingly difficult to locate the right information at the right time. In response to the identified need for improved users ’experience by personalising what they see and using Web 2.0 as a novel platform for users ’participation, the strenuous mission of expert identification is an upcoming trend. Since the sites have a large number of active users sharing their thoughts and ideas, it is very difficult to identify who is correct and who to believe i.e, to identify the expert. As a solution to this problem we propose and consider an archetype to identify an expert by analysing the sentiment from a popular microblogging site, Twitter, where users post their ideas, views and opinions about "everything". We propose the "Group Expert System" that identifies an Expert in the real-time microblogging site by analysing the sentiment of each inter-related tweet in PHP language. The system does so by first analysing the sentiment of each tweet along with the comments received on it, evaluating the tweet score and ranking the tweets, and finally declaring the user an expert whose tweet has the highest rank score. Also, we have demonstrated the efficacy of the proposed model using some tweets retrieved from twitter using its API in the paper to substantiate the result in this paper.