Web Scraping Service & OVR Classification based on Twitter in Machine Learning

Many social media, like Twitter, Facebook and etc, are evolving to become a source of information for people to scrape varied kinds of data, since microblogs on which users post real time messages shows millions of opinions about their attitudes or sentiment towards hot topics and current issues. Recently, I decided to learn how Regional sentiment analysis can help people to make specific decisions or policy strategies for different regions. Notably, Tweets scraped from Twitter can provide tremendous real-time data for our analysis. In my approach, I develop a Twitter Sentiment Classifier, which will classify a scraped tweet into three main polarities: Positive, Negative and Neutral. To make our analysis more straightforward and clear, I will only extract certain data fields related with one tweet.