Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark
Keywords:
Big data, classification, Map-ReduceAbstract
Twitter is free social networking micro blogging service. In that micro-blogging allows to registered members to broadcasting the short posts also called tweets. It can broadcast the tweets by using multiple platforms and devices. Twitter member replies to tweets. Existing system focuses on document level sentiment analysis that means they used Hadoop Framework for concerning moving or product reviews. In that system web pages or blocks on which posts are published therefore in that system complexity of document level opinion mining many efforts have been made towards the sentence level sentiment analysis. The existing systems classify the accuracy only one word. This process is time consuming due to documentation. In the system which we are devolve in that we used spark framework instead of Hadoop framework. Due to the use of Spark Framework, garbage or unclean data are removing. So that user gets better efficiency and less time required for processing, than earlier system.
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