Real Time event occurrence system with particular opinion using Mapreduce Framework for Excavating Twitter Data

  Miss.H.A.Patil,  Prof.S.A.Joshi

Social media is identical importance in today’s life, As everyone could not be able to meet with each other physically. But lots of social websites are solving the problem of above communication (e.g. Twitter).Twitter has lots of tweets everyday on different topics. So storing and maintaining that much amount of data is biggest Achievement. This can be done with the help of Big data which is user friendly and better for scalability. This paper proposed a new system that delivers huge dataset of social media site such as ‘Twitter’. For the operational purpose Mapreduce framework is used which is supporting for multitasking. MongoDB is an open-source document database, and leading NoSQL database, which is utilized in this implementation. This paper gives the supportive solution with a algorithm that is extension of big data algorithm. In this framework application helps to process data as tweets retrieved from Twitter site. When actual processing is taken place for tweets, it involves mining of metadata of tweet, geocoding the physical address in a tweet, examining the sentiment of content in the tweet text and extracting the significant and key phrases from a text. Implemented system integrates connection with actual dataset and retrieves tweets from Twitter. To do the sentiment analysis of data OpenNLP is used with mapreduce framework. As previous study has less performance for dynamic data. We have tried to solve this problem through the use of above mentioned technology.

Keywords- Mapreduce framework; MongoDB, OpenNLP; Big data algorithm; Twitter

Unique Identification Number - IJEDR1703044
Page Number(s) - 316-321
Pubished in - Volume 5 | Issue 3 | July 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Miss.H.A.Patil,  Prof.S.A.Joshi,   "Real Time event occurrence system with particular opinion using Mapreduce Framework for Excavating Twitter Data", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.316-321, July 2017, Available at :http://www.ijedr.org/papers/IJEDR1703044.pdf