Abstract

Today the era is of data driven decision making. For any business it has become essential to listen what their customer's experience is? What they exactly want? How is their brand performing? Are they happy customers? How they can retain them and reduce the rate of churn and convert it into revenue? What's their user base? What is the age segment which gives them the most of the business? Depending upon the answers of all the above questions are we capable to take a decision which can increase the profits? Are all the answers helping businesses to arrive to some conclusion on the basis of which one can make valid decision? What is the truth value of this conclusion? In this paper we are trying to propose a framework which will help businesses to deduce inference from what the customers are talking about their brand and accordingly they can design a new strategy to save, retain and grow their business by taking effective decisions.

References

• Leveraging Sentiment Analysis for Topic Detection IEEE Keke Cai IBM China Res. Lab. , Beijing; Spangler, S. ; Ying Chen; Li Zhang
• Cambria, E. ; Nat. Univ. of Singapore, Singapore, Singapore ; Schuller, B. ; Yunqing Xia ; Havasi, C
• Sentence-Based Sentiment Analysis for Expressive Text-to-Speech IEEE Trilla, T. ; Campus La Salle, Grup de Recerca en Tecnologies Media, Univ. Ramon Llull, Barcelona, Spain; Alias, F.
• Automatic sentiment analysis of Twitter messages IEEE.