Sentiment Analysis using Machine Learning through Twitter Streaming API - Akilandeswari, P; Harshita, R; KO.M, Sumanth. (International Journal of Engineering and Technology, 2018)
Social media allows to share the experiences with many best suggestions and provides opportunities to share the ideas about any topics at any time. In the current trending, twitter is used to gather different kinds of information as user need and it is a social network service which enables the user for better communication and gaining of knowledge. Accurate representation of the user interactions can be done based on the facts sematic content. The pre-processed tweets which are stored in database are been identified and classified whether it relates to the user keywords related posts. The best suggestion using polarity can be predicted using the user keywords. For the interactive automatic system which predicts the tweets posted by the user this system deals with the challenges that appears during the sentimental analysis. It deals with effective study prior to the subjective information. The basic task in this is to identify the polarity of a given tweet in the sentence whether it is positive, negative or neutral. However the polarity of the tweets has been identified, it was difficult for us to check with the meaningless data. To address this challenge the extracted tweets are been pre-processed by replacing the full form instead of short term words. The better performance can be achieved using more training data. However the analysis was frequently done using the previously stored data, it was a challenging task to do it using the streaming data. There are very few works related to the sentiment analysis using online streaming data. In this paper, we propose that the sentiment analysis can be improved using the online streaming data. For online streaming data all the data related to the given topic will be collected according to the current data in the twitter. For better up-to-date analysis, the streaming data is used and can achieve better results. In contrast by conducting the continuous learning from the streaming data, this approach provides better results than the traditional way of using the training data and it achieves the overall performance and computational efficiency.Â Â