A BigData approach for sentiment analysis of twitter data using Naive Bayes and SVM Algorithm Data mining and sentiment analysis are two most versatile research areas in field of real time knowledge extraction. Real time twitter data analysis can plays very crucial role to observe the thinking and view point of people and users. Nowadays, social networking sites have become centric points to share your thoughts and viewpoints. Analysis of social networking data can help a lot to observe trend of society. It can also help to derive user interest and hidden activities. Sentiment analysis is the approach to determine whether piece of writing is positive, negative or neutral. It also help to derive user opinion and attitude of writer. Sentiment analysis of twitter user can help to track eventual view point of user. Country wise accumulative view point can help to derive overall opinion of country citizens and their thinking criteria. This work has proposed sentiment analysis model to observe positive and negative view point of different countries based on sentiment analysis approach. The complete work will be implemented using Hadoop Ecosystem to perform parallel processing on large data.

ExtractionBased Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method The field of sentiment mining and text summarization has evoked the interest of many scientists and researchers over the last few years, as the textual data has become useful for many realworld applications and challenges. Sentiment Analysis and Opinion Mining is the most popular field for analyzing and discovering insights from text data from various sources, such as Facebook, Twitter and Amazon, Zomato, etc. It involves a computational study of an individualxe2x80x99s behavior in terms of buying interest and then extracting his opinions on the business entity of the company. This entity can be viewed as an event, individual, blog post or product experience. Scholars in the fields of natural language processing, data mining, machine learning and others have tested a variety of methods for automating sentiment analysis. These reviews are increasing on a daily basis, as a result of which the summarization of the reviews plays a role where the text is summarized as needed, which provides useful information from a large number of reviews. It is very difficult for a human being to extract and interpret useful data from a very large file. In the text analysis, the value of sentences is decided on the basis of the linguistic characteristics of sentences. This paper provides a comprehensive review of current and past work on sentiment analysis and text description. In this research work, a new hybrid classification system is proposed based on coupling classification methods using arcing classifiers and their quality is evaluated within terms of accuracy. The Classifier Collection was constructed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). The proposed work consists of a comparative study of the efficacy of the ensemble technique for sentiment classification. The feasibility and benefits of the proposed approaches are demonstrated by a restaurant review that is widely used in the field of sentiment classification. A wide range of comparative studies is performed and, ultimately, some indepth analysis is addressed and conclusions are drawn on the efficacy of the ensemble technique for sentiment classification