An interpretation of sentiment analysis for enrichment of Business Intelligence

Sentiment analysis plays a very important role in BI's (Business Intelligence) applications which has been evident in the recent market activities. Towards sentiment analysis for most of the popular websites like Amazon, Facebook, Twitter necessitate the review of the customers which are used as a feedback. It's play very important role for product review, Business intelligence as well as in decision making. The main problem that arises to the point of view of users/customers is that, it is practically in-feasible to read all those online reviews one by one, because some of the products might have tens of thousand reviews. In this paper, reviews are collected from the sources like Amazon, Flipkart, and then used a method to combine both NLP (Natural Language Processing) and machine learning approach. Word sense disambiguation is also considered for this study. An improvised lesk algorithms is used for removing noise in the data. Different types of data have different types of properties and therefore are suited to different techniques correspondingly. This problem is closely related to the large scale nature of social networks and the necessity to perform aggregation operations, which results in the form of Pie-Chart. Thus, we aggregate millions of reviews into more user-friendly format.