| dc.description.abstract | In the research of semantic sentiment analysis, it will normally use some factor rules such as the utilization of emotional keywords and the emotional rules defined manually to increase the accuracy. Because of the demand for large amounts of data and the training take lots of time, these manual factors will usually make the construction of system unportable and decrease efficiency. In this thesis, based on the above demands, we propose a semantic sentiment analysis system, and it also have better quality and increase efficiency.
The system structure of this thesis is organized as follows. First, the data training: It is the research of emotion and emotion psychology. According to the linguistic definition such as HowNet and CKIP technical report, we could make the emotional rules to generate the sparse representation characteristic, and build the sparse representation dictionary. By solved the sparse coefficient, return the dictionary and coefficient of two categories to original vector respectively. Then calculate the error with original vector, the dependent category which is obtain minimum error. Second, the input topic and the obtainment of comments: It present how to get the comments of the hot topic in the internet forum. Finally, the data classification: we will analyze the accuracy of classified topics by the result of data training. Besides, the experimental results will identify the hot topic as the implementation of semantic classification models. | en_US |