Map-Reduce Technique for sentiment analysis of twitter data
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As per some current studies, public opinions expressed in social media may be connected with various social issues. To find out what actually can be discovered in social media data, we need data mining. But traditional data mining has many limitations as size of datasets. So, new data mining approaches that can handle massive amount of data have recently been referred to as big data algorithms. In this paper a new system is proposed that delivers large database of Social Networking Site (SNS) called 'Twitter'. Processing the tweet involves extraction of metadata of tweet, geocoding the physica...
As per some current studies, public opinions expressed in social media may be connected with various social issues. To find out what actually can be discovered in social media data, we need data mining. But traditional data mining has many limitations as size of datasets. So, new data mining approaches that can handle massive amount of data have recently been referred to as big data algorithms. In this paper a new system is proposed that delivers large database of Social Networking Site (SNS) called 'Twitter'. Processing the tweet involves extraction of metadata of tweet, geocoding the physical address in a tweet, analysing the sentiment of content in the tweet text and extracting the considerable and key phrases from a text. After all the Information Extracted and NER (Named Entity Recognition) text analysis from tweet, are stored into a MongoDB database, as it is more scalable and more flexible among others of NoSQL databases
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