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A Big Data Processing Framework for Polarity Detection in Social Network Data
Victor P,
Published in IEEE
Pages: 291 - 295
Big Data refers to the extremely big datasets that are produced from different areas which exhibits certain trends and associations. Major areas of big data include medical data, sensor data, social networks such as facebook, twitter, youtube etc. Among this, social networks produce large amount of data per millisecond which can be analysed for several predictive and analytic purposes. Tweets produced by twitter is used in sentiment analysis and polarity detection that helps in identifying the attitude, polarity of words, text or documents. Applying polarity detection in big data is a tedious task as it includes both historical and streaming data. Several frameworks have been proposed for analysing both historical and streaming data in big data. In this paper, lambda architecture for polarity detection of tweets has been proposed which analyses both streaming and historical data. Both the data can be analysed in parallel and used for certain predictive and analytic purposes. © 2019 IEEE.
About the journal
JournalData powered by Typeset2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)
PublisherData powered by TypesetIEEE
Open AccessNo