A Novel Approach to Optimize the Performance of Hadoop Frameworks for Sentiment Analysis

In today‘s highly developed world, every minute, people around the globe express themselves via various platforms on the Web. And in each minute, a huge amount of unstructured data is generated. Such data is termed as big data. This huge amount of raw data can be used for industrial, social, economic, government policies or business purpose by organizing according to our requirement and processing. In view of aforesaid context, Sentiment analysis in relation to twitter data gains enormous importance. Sentiment analysis offers itself as a good approach in classifying the opinions formulated by individuals into different sentiments such as, positive, negative, or neutral. Hence there is a need for state-of-the-art tools and techniques to be developed for sentiment analysis. An Apache Hadoop framework is one such option that supports distributed data computing and has been commonly adopted for a variety of use-cases. In this work, we identify factors affecting the performance of sentiment analysis algorithms based on Hadoop framework and proposes an approach for optimizing the performance of sentiment analysis.

we need sentiment analysis framework for facebbok, twitter and youtube comment datasets.