Study of Sentiment Analysis Using Hadoop

In the current world of Internet people express themselves, present their views and feelings about specific topics or entities using various social media application. These posts from users present a huge opportunity for the organizations to increase their market value by analyzing the posts and using information in decision making. These posts can be studied using various machine learning and lexicon-based approaches for extracting its sentiments. With more and more people moving to internet, huge data is being produced every second and challenge is to store this large data and process it efficiently in real time to infer knowledge from this data. This paper presents different approaches for real-time and scalable ways of performing sentiment analysis using Hadoop in a time efficient manner. Hadoop and its component tools like MapReduce, Mahout, and Hive are being surveyed in different scholar articles for this paper.