  Rapid increase in the volume of sentiment rich social media on the web has
resulted in an increased interest among researchers regarding Sentimental
Analysis and opinion mining. However, with so much social media available on
the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast
amount of sentiment data available now a days. The main focus of the research
was to find such a technique that can efficiently perform sentiment analysis on
big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis
was performed on a large data set of tweets using Hadoop and the performance of
the technique was measured in form of speed and accuracy. The experimental
results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
