Efficient and Parallel Framework for Analyzing the Sentiment

With the advent of Web 2.0, user-generated content is led to an explosion of data on the Internet. Several platforms such as social networking, microblogging, and picture sharing exist that allow users to express their views on almost any topic. The user views express their emotions and sentiments on products, services, any action by governments, etc. Sentiment analysis allows quantifying popular mood on any product, service or an idea. Twitter is popular microblogging platform, which permits users to express their views in a very concise manner. In this paper, a new framework is crafted which carried out the entire chain of tasks starting with extraction of tweets to presenting the results in multiple formats using an ETL (Extract, Transform, and Load) big data tool called Talend. The framework includes a technique to quantify sentiment in a Twitter stream by normalizing the text and judge the polarity of textual data as positive, negative, or neutral. The technique addresses peculiarities of Twitter communication to enhance accuracy. The technique gives an accuracy of above 84% on standard datasets.