Sentiment Analysis
aims to determine the attitude of a speaker or a writer with respect to some topic
or the overall contextual polarity of a document. Tweets are frequently used to
express a tweeter\'s emotion on a particular subject. There are firms which
poll twitter for analyzing sentiment on a particular topic. The challenge is to
gather all such relevant data, detect and summarize the overall sentiment on a
topic. The social media data includes the collection of unstructured raw data
from various social media posts & blogs such a Twitter and needs to refine
the data to get the results which are most important for understanding the
sentiment of a product or service is either positive, negative or neutral. The
steps for extracting sentiment data from Twitter and analyzing the performance
of a recent iron man3 movie release. we can mine twitter, Facebook and other
social media conversations for sentiment data about a company products or
movies etc is used to make targeted, real-time, decisions that increase market
share. In our project we proposed an architecture, where the raw data taken
from twitter is classified and added to HDFS using Naïve Bayesian classifier.
Further we process HIVE queries on the sentimental data to catalogue positive,
negative and neutral tweets. Subsequently the analysis is illustrated using BI
tools. The main advantage of our project is we can visualize divergent tweets
on the given attributes and data set according to one’s choice in a map view. -See more at:
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