Multiple-classifiers Opinion Sentence Recognition in Chinese Micro-blog Based on D-S Theory
Guo Yun-lon · Jisuanji gongcheng · 2014
With the development and popularity of the new technology and social network, the data volume of micro-blog users surge sharply. Related research causes increasing attention from both academia and industry. This paper proposes a new statistical method on feature extraction. Classification performances of different schemas such as Support Vector Machine(SVM), Naive Bayes and K-Nearest Neighbour(KNN) are analyzed carefully. It proposes a combined model based on D-S theory to take the advantages of different classifiers. A series of experiments based on the Chinese Micro-Blog data provided by CCF NLPCC 2012 are conducted, and it gets the average estimate 72.7% in precision, 61.5% in recall and 64.7% in F-measure of NLPCC 2012 as a baseline. Experimental results show that the method can achieve significant enhancement in both recall and F-measure with 70.6%, 89.2% and 78.9%, respectively, and F-measure is even 0.5% higher than the best result of NLPCC 2012.