Semantic Orientation of Sentiment Analysis on Social Media (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013)
User Generated Contents on social media, such has Blogs, Forums, YouTube, Twitter, Facebook and so on contains opinions or sentiments generated by the users about the object, such has product reviews, movie reviews, book reviews etc. The texts in these social media sites are short and generated constantly. These contents are well suited for knowledge discovery. The purpose of this paper is to locate, extract, classify and summarize the customer opinion about the products from the social media site YouTube. The proposed framework determines the semantic orientation of opinion expressed on product features as positive, negative or neutral. The system is also integrated with a visualization module to present feature based summary of user generated contents.