摘要
文本倾向性分析已成为当前自然语言处理领域的研究热点,其研究成果具有极高的应用价值。针对网络在线中文评论的特点,基于领域本体与情感词典对商品评论倾向性进行分析。其主要思想是首先构建面向商品论坛的领域本体;其次利用情感词典与上下文极性算法计算情感词极性;再次通过将本体与SBV算法相结合,实现评价对象和评价词的二元组抽取;最后完成句子的倾向性分析。实验结果表明,有效提高了句子级倾向性分析的准确率。
Text propensity analysis has currently become the focus of research in natural language processing field, its research results have the extremely high application value.Aiming at the characteristics of online internet Chinese reviews, in this paper we analyse the tendencies of product reviews based on domain ontology and sentiment lexicon.Our main idea is to build the product forum-oriented domain ontology first. Then we calculate the polarities of sentiment words by using the sentiment lexicon and context polarity algorithm.Thirdly, by combining domain ontology with SBV algorithm we realise the extraction of 2-tuple of evaluation object and evaluation words.Finally, we complete the analysis of sentence propensity.Experimental results show that it improves the accuracy of propensity analysis in sentence level effectively.
出处
《计算机应用与软件》
CSCD
北大核心
2014年第12期104-108,194,共6页
Computer Applications and Software
基金
国家自然科学基金项目(51278400)
陕西省自然科学基金项目(2012JM8042)
陕西省教育厅专项科研项目(12JK0940)
技术转移促进工程项目(CXY1348-(1))
关键词
倾向性分析
领域本体
情感词典
上下文极性
评价对象抽取
Propensity analysis
Domain ontology
Sentiment lexicon
Context polarity
Evaluation object extraction
