Abstract: Sentiment Analysis also termed as opinion mining is a process of obtaining knowledge on a specific subject from source content. Sentiment Analysis uses techniques from various disciplines such as machine learning, text mining and natural language processing. Data analysis is a process of extracting information from knowledge source for the purpose of supporting decision making process. The various phases in data analysis are data extraction, cleaning, transformation, integration and finally knowledge extraction. In this paper, a model has proposed to address the two issues such as sentiment analysis and data analysis on the data which is collected from Twitter. Various dimensions in the form of a new science behind the famous blog service known as Twitter are extracted. Twitter blog is used by the users to post the micro messages and their opinions know as tweets on a particular subject. The results are obtained for the tweets on the topic and classify them into positive, negative and neutral opinions. It is also analyzed the tweets arriving source, aggregate number of tweets generated by the users on a particular topic along a time series, number of tweets specific to language from different parts of the world.
Keywords: Data Analysis, Sentimental Analysis, Twitter, Map-reduce, Time series analysis, Data visualization.