Applying Clustering Techniques for Efficient Text Mining in Twitter Data

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dc.categoryJournal Article
dc.contributor.authorElizabeth Shanthi, I
dc.date.accessioned2017-03-29T23:21:14Z
dc.date.available2017-03-29T23:21:14Z
dc.date.issued2015
dc.departmentComputer Scienceen_US
dc.description.abstractBCnowledge is the ultimate output of decisions on a dataset. The revolution of the Internet has made the global distance closer with the touch on the hand held electronic devices. Usage of social media sites have increased in the past decades. One of the most popular social media micro blog is Twitter. Twitter has millions of users in the world.^In this paper the analysis of Twitter data is performed through the text contained in hash tags. After Preprocessing clustering algorithms are applied on text data. The different clusters igjj compared through various parameters. alization techniques are used to portray the results from which inferences like time series and topic flow can be easily made. The observed results show that the hierarchical clustering algorithm performs better than other algorithms.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2327
dc.langEnglishen_US
dc.publisher.nameInternational Journal of Data Mining Techniques And Applicationsen_US
dc.publisher.typeInternationalen_US
dc.titleApplying Clustering Techniques for Efficient Text Mining in Twitter Dataen_US
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