抽象的な

Formation of Smart Sentiment Analysis Technique for Big Data

Manisha Shinde-Pawar

Many of the top digital e-textbook companies employ big data in the form of analytics to not only measure customers buying habits, but also to provide the organizations with measurable data. Analytics are more important than just clicking on a buy button. Analyzing the voluminous data at an instant of time in memory to take right decisions is great challenge. To avoid such situations the basic need is to study sentiments while taking decisions. Here data analytics can help to analyze such big data. This has given rise a thirst for carrying out the study on sentiment analytics, big data and use of some smart algorithm to discover correct sentiments or opinions from unstructured big data. The approach uses natural language processing techniques of Artificial Neural Network to extract features of interest from textual data retrieved from a micro blogging platform in real-time and, hence, generate appropriate executable code for the Decision Science and get predetermined means of social communication. So by enriching semantic knowledge bases using Fuzzy Logic (for fitness approximation) for Opinion Mining in Big Data Applications with predetermined means, suggested user action decisions can be improved.