Sentiment analysis in news is different from normal text sentiment analysis. News usually have a specific topic, a focus semantic emotion, therefore, this paper, based on the principal of using Emotion Dependency Tuple (EDT) as the basic unit of news emotion analysis, resolves topic sentiment analysis in news into three progressive sub-problem, namely, topic sentence recognition, EDT extraction and topic sentiment analysis. We use an improved TF-IDF and cross entropy to extract feature set of topics. Then, based on space vector model, calculate the topic association of a sentence and extract topic sentence. Finally, we construct topic sentence based on EDT and complete clustering of news topic sentiment. This method is evaluated using COAE2014 dataset, and differential means shows that our results close to the best results. This shows that the topic based EDT could effectively improve performance of sentiment analysis in news.
Currently, most sentiment analysis of microblog has been focused on coarse-grained sentiment analysis, but fine-grained sentiment is better for reflecting the opinion of the public when they are facing the social focus. Therefore, a hybrid strategy which is a combination of Naïve Bayesian and two-layer CRFs is put forward, which has been applied to the fine-grained sentiment analysis of Chinese microblog. First, microblog is classified into two types: sentiment and non-sentiment by using Naïve Bayesian classification algorithm. And then the first-layer CRFs model is built for the topic emotional sentence. Finally CRFs algorithm is used again to do multi-classification to assign a specific sentiment category. Experimental results show that a good result in sentiment identification based on the combination of Naïve Bayesian and CRFs, and also show the advantage of the combination of Naïve Bayesian and CRFs interrelated with emotional sentence extraction based on CRFs.
[ Chen, Ji Feng ; Liu, Shu Kun ] Department of Computer, Hunan International Economics University, Chang sha, China;[ Yang, Xiao Hua ] Department of Computer Science and Technology, University of South China, Heng yang, China
Compile - Software quality - Software quality assurance - Theoretical models - Trace file - Work process
[ Yang, Xiaohua ; Liu, Zhaohui ] School of Computer Science and Technology, University of South China, Hengyang;[ Yang, Xiaohua ; Liu, Zhaohui ] 421001, China;[ Wu, Zhiqiang ] Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu;[ Wu, Zhiqiang ] 610041, China;610041, China
Digital reactor protection systems - Hardware and software - Instrumentation and control - Reactor protection systems - Safety critical systems - Safety requirements - STPA - System safety
We studied the electronic communication of knowledge users collaborating on a community and found that their work and interactions were mediated by the use of tag. Drawing on these, we found social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (books, pictures, films etc.). Automatic discourse classification according to tag in social information sharing, transfer and knowledge communication provided a higher level of service quality. By investigating user behavior in Douban community, the relationship between social tagging and user behavior was studied. The results showed that, given a set of objects, and a set of tags applied to those objects by users, we can predict whether a given tag could/should be applied to a particular object by user behavior.
[Yang, Xiaohua; Liu, Yongbin; Zou, Yinfeng; Yu, Ying; Ouyang, Chunping] School of Computer Science and Technology, University of South China, Hengyang;[Yang, Xiaohua; Liu, Yongbin; Zou, Yinfeng; Yu, Ying; Ouyang, Chunping] 421001, China;421001, China
Words similarity;HowNet;Pearson correlation coefficient Spearman's coefficient
International Review on Computers and Software,2011年6(6):1117-1121 ISSN：1828-6003
[ Chen, Jifeng ; Liu, Shukun ] Department of Computer Science and Technology, Hunan International Economics University, Changsha 410205, China;[ Yang, Xiaohua ] Department of Computer Science and Technology, University of South China, Hengyang 421001, China