读者视角的跨领域隐式情感分析理论及关键技术研究

项目名称： 读者视角的跨领域隐式情感分析理论及关键技术研究

项目编号： No.61502545

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 计算机科学学科

项目作者： 饶洋辉

作者单位： 中山大学

项目金额： 22万元

中文摘要： 互联网蕴含了众多用户交流的观点与情感，从中分析读者用户的隐式情感表达在挖掘公众兴趣与需求，了解社会发展动态，提高服务质量等方面都非常重要。但对于数据分布不一致的跨领域文本，词形相似的语句或文档可能引发读者不同甚至对立的情感反馈，因此给精准地分析其隐式情感带来了极大的挑战。本项目将用句子、篇章及组合语义分析方法，重点研究如下内容：1）设计句子层的规范化与自动标注方案，以补充跨领域隐式情感分析训练数据集；2）建立篇章层的语义分析模型，实现对跨领域隐式情感的有效抽取；3）提出基于多层组合语义的分类算法，以提升跨领域隐式情感分类的精准度。研究成果不仅将解决隐式情感标注的领域及句子语义不足、跨领域的隐式情感抽取及分类不准等研究难题，还能为开发一个具有跨领域隐式情感标注、抽取及分类功能的实用系统奠定基础。

中文关键词： 情感分析；隐式情感；跨领域情感；舆情分析；观点挖掘

英文摘要： As many users tend to convey their opinions and emotions online, it is important to mine their implicit emotions. Implicit sentiment analysis of readers is benefit to identify the general public’s interest or demand, to track the dynamic status of social development, and to improve the quality of services. However, different or contrary reader emotions can be present across the span of a sentence or document with similar words for cross-domain corpus, hence cause low accuracy of implicit sentiment analysis. In this project, based on the sentence and document level, and compound semantic analysis methods, the main tasks and objectives are to: 1) design the standard and automated implicit sentiment annotation schemes at the sentence level, so as to enrich the training data of cross-domain implicit emotions; 2) develop the document level semantic analysis model for extracting cross-domain implicit emotions effectively; 3) propose the cross-domain implicit sentiment classification algorithm based on compound semantic, so as to improve the accuracy. The research in this project will tackle the challenging issues of lacking domain information and sentence level semantic in implicit sentiment annotation, in addition to improve the accuracy of implicit sentiment extraction and classification. It will also lay the foundation for developing utility systems of cross-domain implicit sentiment annotation, extraction and classification.

英文关键词： sentiment analysis;implicit sentiment;cross-domain sentiment;public opinion analysis;opinion mining