http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Ouyang Chunping,Zhou Wen,Yu Ying,Liu Zhiming,Yang Xiaohua (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Opinion Objects Identification and Sentiment Analysis
Ouyang Chunping,Liu Yongbin,Zhang Shuqing,Yang Xiaohua 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
Sentiment analysis of reviews has been the focus of recent research, which also has been attempted in different domains such as product reviews, movie reviews, and customer feedback reviews. Most sentiment analysis of reviews focused on extracting overall evaluation for a single product which makes difficult for a customer to know all the features of product and make a decision. Thus, mining this data, identifying the user opinions about different features and classify them is an important task. This paper is devoted to identify opinion object from short comments, and analyze sentiment of product based on features-level. CRFs model based on word embedding feature is adopted by identifying opinion object, which obtains a satisfied results. In addition, calculate rules based on syntax parsing are proposed to accomplish features-level sentiment analysis which extracts user’s opinion on many aspects. Experimental results using short comments of movies show the effectiveness of our approach.
-
A Hybrid Strategy for Fine-Grained Sentiment of Microblog
Ouyang Chunping,Luo Lingyun,Zhang Shuqing,Yang Xiaohua 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6
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.
-
Topic Sentiment Analysis in Chinese News
Ouyang Chunping,Zhou Wen,Yu Ying,Liu Zhiming,Yang Xiaohua 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.11
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.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
