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Journal of Software:2020.31(6):1723-1746

跨领域文本情感分类研究进展
赵传君,王素格,李德玉
(山西财经大学 信息学院, 山西 太原 030006;山西大学 计算机与信息技术学院, 山西 太原 030006;计算智能与中文信息处理教育部重点实验室(山西大学), 山西 太原 030006)
Research Progress on Cross-domain Text Sentiment Classification
ZHAO Chuan-Jun,WANG Su-Ge,LI De-Yu
(College of Information, Shanxi University of Finance and Economics, Taiyuan 030006, China;School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education (Shanxi University), Taiyuan 030006, China)
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Received:September 12, 2019    Revised:February 29, 2020
> 中文摘要: 作为社会媒体文本情感分析的重要研究课题之一,跨领域文本情感分类旨在利用源领域资源或模型迁移地服务于目标领域的文本情感分类任务,其可以有效缓解目标领域中带标签数据不足问题.从3个角度对跨领域文本情感分类方法行了归纳总结:(1)按照目标领域中是否有带标签数据,可分为直推式和归纳式情感迁移方法;(2)按照不同情感适应性策略,可分为实例迁移方法、特征迁移方法、模型迁移方法、基于词典的方法、联合情感主题方法以及图模型方法等;(3)按照可用源领域个数,可分为单源和多源跨领域文本情感分类方法.此外,还介绍了深度迁移学习方法及其在跨领域文本情感分类的最新应用成果.最后,围绕跨领域文本情感分类面临的关键技术问题,对可能的突破方向进行了展望.
Abstract:As an important research topic in social media text sentiment analysis, cross-domain text sentiment classification aims to use the source domain resources or model transfer to serve the target domain text sentiment classification task, which can effectively solve the problem of insufficient data marking in specific domains. In order to solve the problem of cross-domain sentiment adaptation, this article summarizes the existing studies of cross-domain sentiment classification from three perspectives, i.e., (1) it can be divided into transductive and inductive cross-domain sentiment classification methods according to whether there is labeled data in the target domain; (2) it can be divided into instance transferring based, feature transferring based, model or parameters transferring based, sentiment dictionary based, joint sentiment topic based, and graph model based methods according to different sentiment adaption strategies; (3) it can also be divided into single-source domain and multi-source domains of cross-domain sentiment classification according to the number of available source domains. In addition, it is also introduced that a new approach of deep transfer learning to solve cross-domain sentiment classification problems, and summarize its latest research results in cross-domain sentiment classification. Finally, the challenges are combined with key issues of current cross-domain sentiment classification technology and further study directions are pointed out.
文章编号:     中图分类号:TP391    文献标志码:
基金项目:国家自然科学基金(61906110,61632011,61573231,61672331,61432011,61603229);山西省高等学校科技创新项目(2019L0500);山西省应用基础研究计划(201901D211414);山西省高等学校优秀成果培育项目(2019SK036) 国家自然科学基金(61906110,61632011,61573231,61672331,61432011,61603229);山西省高等学校科技创新项目(2019L0500);山西省应用基础研究计划(201901D211414);山西省高等学校优秀成果培育项目(2019SK036)
Foundation items:National Natural Science Foundation of China (61906110, 61632011, 61573231, 61672331, 61432011, 61603229); Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi (STIP) (2019L0500); Shanxi Application Basic Research Plan (201901D211414); Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi (CSREP) (2019SK036)
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赵传君,王素格,李德玉.跨领域文本情感分类研究进展.软件学报,2020,31(6):1723-1746

ZHAO Chuan-Jun,WANG Su-Ge,LI De-Yu.Research Progress on Cross-domain Text Sentiment Classification.Journal of Software,2020,31(6):1723-1746