Automated Real Time Sentiment Classification of Twitter Data

The growth in the area of sentiment analysis and opinion mining has been quick and it also aims to explore the text or opinions on different social media through various machine-learning techniques with the sentiment, confidence, polarity calculations or subjectivity analysis. Users generally tend to express their real feelings on social sites such as Twitter, Facebook, Instagram etc on interesting topics such as brands, products and celebrities etc. But despite the use of various machine learning techniques, there is a dire need of a state-of-the-art approach. This paper tries to contribute to solving these challenges and the creation of an automated system is proposed which reduces the manual labour of preprocessing and filtering of data and the time gap. It tries to integrates various algorithms and results in a module which can be used to determine the sentiment of any text.