Sentiment Analysis by Sequence of Word Patterns Using Scale of Pivot Indicators for Twitter Streams

The opinion mining has wide role in identifying the target audience opinion about relies on opinion of the target users on products, events, current affairs and many more. Among these twitter platforms has widely adapted by the target audience. Many of the contemporary methods have endeavor to perform opinion mining on tweets placed under twitter trends. However, the contemporary models, which are particularly machine learning models have limited to static data of the tweets to train and test. However, the tweets related a twitter trend are continuous, a novel sentiment analysis by sequential patterns of the words, which derives pivot indicators to predict the sentiment polarity of the twitter streams. The experimental study carried as tenfold cross validation on both proposed and contemporary model. Performance analysis is evincing that proposed model is outperforming the contemporary model.