Are your students upset or angry with what you said in the class? How many of your friends are offended by what you posted in a forum? Would you like to check how your post would be perceived by others even before you post it? These are some of the innovative outcomes of sentiment analytics described in this demo.
This project presents a knowledge representation framework for natural language understanding. Here we propose an automated knowledge acquisition mechanism that mirrors information extraction in human-human interaction. This framework utilizes knowledge based automatic role labeling and automatic concept learning together with a conceptual structure that captures intent and context. The resulting framework is to be used to improve the agent’s ability to engage in social interaction with humans.
Fakinlede, I., Kumar, V., Wen, D., (2013), Knowledge Representation for Context and Sentiment Analysis. Workshop on Learning Analytics at the International Conference on Advanced Learning Technologies (ICALT 2013), pp.487-488, Beijing, China.