![]() ![]() Millions of tweets have been classified using the trained model. A Spark-based Naive Bayes training technique is suggested for this purpose unlike prior research, this algorithm does not need any disk access. Using the Apache Spark Parallel Model, this proposed work presents a scalable system for sentiment analysis on Twitter. Most parallel programming frameworks, such as MPI (Message Processing Interface), are difficult to use and scale in environments where supercomputers are expensive. ![]() Supercomputers or parallel or distributed processing are two options for dealing with such large amounts of data. Single node computational methods are inefficient for sentiment analysis on such large datasets. As a result, social media has emerged as the most effective and largest open source for obtaining public opinion. ![]() The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics. ![]()
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