Real Time Sentimental Analysis on Twitter

Real-time analysis is one of the vital things where it is used widely in Big Data Analytics (BDA). Here a cluster or group which is a set of tweets that are created by different users on the Twitter website were developed and intimated to the user about the behavior which is mainly focusing on sentimental analysis. Here, Flume is used to collect the real-time data which actually integrate with the Twitter (website) developer account. To ensure the data security, real-time and fast information processing, the most famous or popular tools that are being used like Flume, Hive and ML algorithms in python to obtain the results more accurately. Both the K-Means and TF-IDF approaches were used, which gives the result in the table format and also contains the values of each tweet or sentence that are being tweeted by a user. Here, the values obtained by using TF-IDF depicts whether it is positive or negative or neutral. The values which are very close to each other form a single group or a cluster. There are three clusters, either positive or negative or neutral. Finally, the output obtained intimates the user that the tweets are either positive or negative or neutral.