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Incorporating multi-level user preference into document-level sentiment classification

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成果类型:
期刊论文
作者:
Li, Junjie;Li, Haoran;Kang, Xiaomian;Yang, Haitong;Zong, Chenqing*
通讯作者:
Zong, Chenqing
作者机构:
[Li, Junjie; Li, Haoran; Kang, Xiaomian] Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China.
[Yang, Haitong] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
[Zong, Chenqing] Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Chinese Acad Sci, Natl Lab Pattern Recognit,Inst Automat, Beijing, Peoples R China.
[Li, Junjie; Zong, Chenqing; Li, Haoran; Kang, Xiaomian] Intelligence Bldg,95,Zhongguancun East Rd, Beijing 100190, Peoples R China.
[Yang, Haitong] 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Zong, Chenqing] U
[Zong, Chenqing] I
Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Chinese Acad Sci, Natl Lab Pattern Recognit,Inst Automat, Beijing, Peoples R China.
Intelligence Bldg,95,Zhongguancun East Rd, Beijing 100190, Peoples R China.
语种:
英文
关键词:
Sentiment classification;deep learning;user preference;hierarchical attention network
期刊:
ACM Transactions on Asian and Low-Resource Language Information Processing
ISSN:
2375-4699
年:
2019
卷:
18
期:
1
页码:
7.1-7.17
基金类别:
The research work described in this article has been supported by the National Key Research and Development Program of China under Grant No. 2017YFB1002103. Authors’ addresses: J. Li, H. Li, and X. Kang, Intelligence Building, No. 95, Zhongguancun East Road, Haidian District, Beijing, 100190, China; emails: {junjie.li, haoran.li, xiaomian.kang}@nlpr.ia.ac.cn; H. Yang, NO.152 Luoyu Road, Wuhan, HuBei, 430079, China; email: htyang@mail.ccnu.edu.cn; C. Zong (corresponding author), Intelligence Building, No. 95, Zhongguancun East Road, Haidian District, Beijing, 100190, China; email: cqzong@nlpr.ia.ac.cn. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2018 Association for Computing Machinery. 2375-4699/2018/11-ART7 $15.00 https://doi.org/10.1145/3234512
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Document-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., wordlevel 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 ...

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