CASIA OpenIR
Incorporating Multi-Level User Preference into Document-Level Sentiment Classification
Li, Junjie1,4; Li, Haoran1,4; Kang, Xiaomian1,4; Yang, Haitong2,5; Zong, Chenqing3,4
Source PublicationACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
ISSN2375-4699
2019
Volume18Issue:1Pages:17
Corresponding AuthorZong, Chenqing(cqzong@nlpria.ac.cn)
AbstractDocument-level sentiment classification aims to predict a user's sentiment polarity in a document about a product. Most existing methods only focus on review contents and ignore users who post reviews. In fact, when reviewing a product, different users have different word-using habits to express opinions (i.e., word-level user preference), care about different attributes of the product (i.e., aspect-level user preference), and have different characteristics to score the review (i.e., polarity-level user preference). These preferences have great influence on interpreting the sentiment of text. To address this issue, we propose a model called Hierarchical User Attention Network (HUAN), which incorporates multi-level user preference into a hierarchical neural network to perform document-level sentiment classification. Specifically, HUAN encodes different kinds of information (word, sentence, aspect, and document) in a hierarchical structure and imports user embedding and user attention mechanism to model these preferences. Empirical results on two real-world datasets show that HUAN achieves state-of-the-art performance. Furthermore, HUAN can also mine important attributes of products for different users.
KeywordSentiment classification deep learning user preference hierarchical attention network
DOI10.1145/3234512
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFB1002103] ; National Key Research and Development Program of China[2017YFB1002103]
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000457155300007
PublisherASSOC COMPUTING MACHINERY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25292
Collection中国科学院自动化研究所
Corresponding AuthorZong, Chenqing
Affiliation1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China
3.Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Chinese Acad Sci, Natl Lab Pattern Recognit,Inst Automat, Beijing, Peoples R China
4.Intelligence Bldg,95,Zhongguancun East Rd, Beijing 100190, Peoples R China
5.152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Junjie,Li, Haoran,Kang, Xiaomian,et al. Incorporating Multi-Level User Preference into Document-Level Sentiment Classification[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2019,18(1):17.
APA Li, Junjie,Li, Haoran,Kang, Xiaomian,Yang, Haitong,&Zong, Chenqing.(2019).Incorporating Multi-Level User Preference into Document-Level Sentiment Classification.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,18(1),17.
MLA Li, Junjie,et al."Incorporating Multi-Level User Preference into Document-Level Sentiment Classification".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 18.1(2019):17.
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