High Dimensional Data Sets Using Advanced Data Engineering Techniques for Sentiment Analysis
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. The difficulties of performing sentiment analysis in this domain can be overcome by leveraging on common-sense knowledge bases. Opinion Mining is a very challenging and promising discipline which is defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The main aim at solving the problems related to opinions about products, reviews ranking in movies, Politian in newsgroup posts, review sites etc. In this paper we are about to cover the source of data from where we take , its classification, evaluation process and then grouping techniques, tools used, and future challenges in opinion mining. Opinion mining consists of various stages such as extraction of data from various sources, text classification, grouping together and then evaluating it to positive or negative or true or false value. On the basis of our survey and analysis of the techniques, we provide an overall picture of what is involved in developing a software system for opinion mining. Any user, buyer or customer rely on the Web for their opinions on various products and services which they have used, it is very important to develop methods to automatically classify and evaluate them. The task of classifying and analyzing such collective data together is known as customer feedback or review data, and is called as opinion mining.