A Unified Framework for Emotional Elements Extraction Based on Finite State Matching Machine

摘要：

　　Traditional methods for sentiment analysis mainly focus on the construction of emotional resources based on the review corpus of specific areas, and use phrase matching technologies to build a list of product feature words and opinion words.These methods bring about the disadvantages of inadequate model scalability, low matching precision, and high redundancy.Besides, it is particularly difficult to deal with negative words.In this work, we designed a unified framework based on finite state matching machine to deal with the problems of emotional element extraction.The max-matching principal and negative words processing can be integrated into the framework naturally.In addition, the framework leverages rule-based methods to filter out illegiti mate feature-opinion pairs.Compared with traditional methods, the framework achieves high accuracy and scalability in emotional element extraction.Expe rimental results show that the extracting accuracy is up to 84%, which has increased by 20% comparing with traditional phrase matching techniques.

关键词： sentiment analysis e-commerce emotional elements extraction finite state matching machine

作者: Yunzhi Tan Yongfeng Zhang Min Zhang Yiqun Liu Shaoping Ma

作者单位: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: Second CCF Conference,NLPCC2013(第二届自然语言处理与中文计算会议)

会议地点: 重庆

会议语种:英文

页码: 60-71

在线出版日期: 2013-11-15（万方平台首次上网日期，不代表论文的发表时间）