In todays extremely developed world, each minute, individuals round the globe specific themselves via numerous platforms on the net. And in every minute, an enormous quantity of unstructured information is generated. This information is within the style of text that is gathered from forums and social media websites. Such information is termed as massive information. User opinions square measure associated with a good vary of topics like politics, latest gadgets and merchandise. Social Networking sites provides tremendous impetus for large information in mining people's opinion. Public API's catered by sites like Twitter provides North American nation with helpful information for studying writer's perspective in terms of of a specific topic, product etc. To distinguish people's opinion, tweets square measure labeled into positive, negative or neutral indicators. This paper provides an efficient mechanism to perform opinion mining by coming up with a finish to finish pipeline with the assistance of Apache Flume ,Apache HDFS, and Apache Hive. Here we proposed to develop a opinion Analysis mechanism to analyze the various polarity of opinions of Twitter users through their tweets in order to extract what they think. Here we have used dictionary based approach for analysis for which we have implemented hive queries through which we can analysis these complex twitter data to check polarity of the tweets based on the polarity dictionary through which we can say that which tweets have negative opinion or positive opinion.
Social Media Network is one of the main source of data for various event detections. Here in this paper a new and efficient method for the Detection of Traffic in Online Social Network Data is proposed using Clustering and Classification of Data. The Planned Procedure applied here is based on SVM Supervised Learning based Clustering of Similar features of Traffic and then classify the Data using J48 Decision Tree to classify number of events performed in the Twitter Traffic. The Planned Procedure is then compared with the Existing Classification approached such as SVM and Naïve Bayes and C4.5, but the technique is more efficient in comparison.
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