中文微博观点句识别及要素抽取研究

研究中文微博情感分析中的观点句识别及要素抽取问题。在观点句识别方面，提出了一种利用微博中的情感词和 情感影响因子计算微博语义情感倾向的新算法；在观点句要素抽取方面，利用主题词分类及关联规则，辅以一系列剪枝、筛选和定界规则抽取评价对象。通过观点句识别和观点句要素抽取结果的相互过滤，进一步提高召回率。实验数据采用第六届中文倾向性分析评测所发布的数据，结果表明，本文方法在观点句识别和要素抽取方面能够取得较好的效果，观点句识别的精确率、召回率入F值分别为95.62%，54.10%及69.10%；观点句要素抽取的精确率、召回率以及F值分别为22.07%，12.66%和16.09%。

Opinion Sentence Identification and Element Extraction in Chinese Micro Blogs

The research aimes at opinion sentence identification and element extraction in sentiment analysis in Chinese micro blogs. In the aspect of opinion sentence identification, the authors propose a new algorithm to compute the micro blog semantic sentiment orientation using sentiment words and emotional impact factors. In element extraction, subject term classification and the association rule are applied, accompanied with a series of pruning, sifting and delimiting rules to extract evaluative objects in micro blogs. Through mutual filtering of opinion sentence identification and element extraction, the recall rate is improved further. The released data of the sixth Chinese opinion analysis evaluation is adopted as experimental data. The results show that the methods perform well in opinion sentence recognition and element extraction. The precision ratio, recall rate, and F -value of opinion sentence identification are 95.62%,54.10% and 69.10%, respectively. The precision ratio, recall rate, and F-value of element extraction are22.07%, 12.66% and 16.09%, respectively.