Research Progress on Cross-domain Text Sentiment Classification

被引：0
|
作者：
Zhao C.-J. [1 ]
Wang S.-G. [2 ,3 ]
Li D.-Y. [2 ,3 ]
机构：
[1] College of Information, Shanxi University of Finance and Economics, Taiyuan
[2] School of Computer and Information Technology, Shanxi University, Taiyuan
[3] Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan
来源：
Zhao, Chuan-Jun (zhaochuanjun@foxmail.com) | 1723年 / Chinese Academy of Sciences卷 / 31期
基金：
中国国家自然科学基金;
关键词：
Cross-domain sentiment classification; Domain adaptation; Research progress; Transfer leaning;
D O I：
10.13328/j.cnki.jos.006029
中图分类号：
学科分类号：
摘要：
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. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1723 / 1746
页数：23
相关论文
共 112 条
  • [81] Zhu E, Huang G, Mo B, Et al., Features extraction based on neural network for cross-domain sentiment classification, Proc. of the Int'l Conf. on Database Systems for Advanced Applications, pp. 81-88, (2016)
  • [82] Yu J, Jiang J., Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification, Proc. of the 2016 Conf. on Empirical Methods in Natural Language Processing, pp. 236-246, (2016)
  • [83] Mao K, Niu J, Wang X, Et al., Cross-Domain sentiment analysis of product reviews by combining lexicon-based and learn-based techniques, Proc. of the Int'l Conf. on High Performance Computing and Communications, pp. 351-356, (2015)
  • [84] Molina-Gonzalez MD, Martinez-Camara E, Martin-Valdivia MT, Et al., Cross-Domain sentiment analysis using spanish opinionated words, Proc. of the Int'l Conf. on Applications of Natural Language to Data Bases/Information Systems, pp. 214-219, (2014)
  • [85] Steyvers M, Griffiths T., Probabilistic topic models, Handbook of Latent Semantic Analysis, 427, 7, pp. 424-440, (2007)
  • [86] Lin C, He Y., Joint sentiment/topic model for sentiment analysis, Proc. of the 18th ACM Conf. on Information and Knowledge Management, pp. 375-384, (2009)
  • [87] He Y, Lin C, Alani H., Automatically extracting polarity-bearing topics for cross-domain sentiment classification, Proc. of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. ACL, pp. 123-131, (2011)
  • [88] Roy SD, Mei T, Zeng W, Et al., Socialtransfer: Cross-domain transfer learning from social streams for media applications, Proc. of the 20th ACM Int'l Conf. on Multimedia, pp. 649-658, (2012)
  • [89] Nelakurthi AR, Tong H, Maciejewski R, Et al., User-Guided cross-domain sentiment classification, Proc. of the 2017 SIAM Int'l Conf. on Data Mining, pp. 471-479, (2017)
  • [90] Xu Z, Sun S., Multi-Source transfer learning with multi-view Adaboost, Proc. of the 2012 Int'l Conf. on Neural Information Processing, pp. 332-339, (2012)
3456789101112