A public opinion keyword vector for social sentiment analysis research

In the Internet era, online platforms are the most convenient means for people to share and retrieve knowledge. Social media enables users to easily post their opinions and perspectives regarding certain issues. Although this convenience lets the internet become a treasury of information, the overload also prevents user from understanding the entirety of various events. This research aims at using text mining techniques to explore public opinion contained in social media by analyzing the reader's emotion towards pieces of short text. We propose Public Opinion Keyword Embedding (POKE) for the presentation of short texts from social media, and a vector space classifier for the categorization of opinions. The experimental results demonstrate that our method can effectively represent the semantics of short text public opinion. In addition, we combine a visualized analysis method for keywords that can provide a deeper understanding of opinions expressed on social media topics.