Data science techniques are becoming increasingly popular these days to improve business outcomes. Hadoop, already showcased the broader use of big data technologies and their impact on businesses. In this new age, the limits of machine learning are constantly being tested as innovators are trying new techniques that decrease human intervention as much as possible. Companies are ready to work with the data they have in Hadoop. Penetration of SQL on Hadoop has been a great help as they have created an environment that has made data accessible to downstream apps and learning algorithms. Machine intelligence is catching up in all spheres, data science is becoming a new trend with data scientists coming in demand. To know more, please follow:

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This article elaborates on the sentiment analysis from tweets using data mining techniques. Instead of using SQL, it shows how to conduct such analysis using a more sophisticated software called RapidMiner. It explains how one can extract Twitter data into Google Docs spread sheet and then transfer it into a local environment utilizing two different methods. The emphasis is on how to amass a decent pool of tweets in two different ways using a service called Zapier, Google Docs and a tool called GDocBackUpCMD, along with SSIS and a little bit of C#. Zapier is used to extract Twitter feeds into Google Docs spread sheet and then copy the data across to local environment to mine it for sentiment trends. Next, it is shown how this data can be analyzed for sentiments i.e. whether a concrete Twitter feed can be considered as negative or positive. For this purpose, RapidMiner as well as two separate data sets of already pre-relegated tweets for model learning and Microsoft SQL Server for some data polishing and storage engine. Read more at:http://bicortex.com/twitter-sentiment-analysis-mining-twitter-data-using-rapidminer-part-1/