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Sentiment Lexicon Construction With Hierarchical Supervision Topic Model
Deng, Gong1; Jing, Liping1; Yu, Jian1; Sun, Shaolong2,3,4,5; Ng, Michael K.6,7
2019-04-01
Source PublicationIEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
ISSN2329-9290
Volume27Issue:4Pages:704-718
AbstractIn this paper, we propose a novel hierarchical supervision topic model to construct a topic-adaptive sentiment lexicon (TaSL) for higher-level classification tasks. It is widely recognized that sentiment lexicon as a useful prior knowledge is crucial in sentiment analysis or opinion mining. However, many existing sentiment lexicons are constructed ignoring the variability of the sentiment polarities of words in different topics or domains. For example, the word "amazing" can refer to causing great surprise or wonder hut can also refer to very impressive and excellent. In TaSI., we solve this issue by jointly considering the topics and sentiments of words. Documents are represented by multiple pairs of topics and sentiments, where each pair is characterized by a multinomial distribution over words. Meanwhile, this generating process is supervised under hierarchical supervision information of documents and words. The main advantage of TaSL is that the sentiment polarity of each word in different topics can be sufficiently captured. This model is beneficial to construct a domain-specific sentiment lexicon and then effectively improve the performance of sentiment classification. Extensive experimental results on four publicly available datasets, MR, OMD, semEvall3A, and semEvall6B were presented to demonstrate the usefulness of the proposed approach. The results have shown that TaSL performs better than the existing manual sentiment lexicon (MPQA), the topic model based domain-specific lexicon (ssLDA), the expanded lexicons(Weka-ED, Weka-STS, NRC, Liu's), and deep neural network based lexicons (nnLexicon, HIT, HSSWE).
KeywordSentiment analysis topic model sentiment lexicon construction opinion mining text mining
DOI10.1109/TASLP.2019.2892232
Language英语
Funding ProjectNational Natural Science Foundation of China[61822601] ; National Natural Science Foundation of China[61773050] ; National Natural Science Foundation of China[61632004] ; Beijing Natural Science Foundation[Z180006] ; Beijing Municipal Science & Technology Commission[Z181100008918012]
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000459536700003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32676
Collection中国科学院数学与系统科学研究院
Affiliation1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
4.City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Peoples R China
5.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
6.Hong Kong Baptist Univ, Ctr Math Imaging & Vis, Hong Kong, Peoples R China
7.Hong Kong Baptist Univ, Dept Math, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Deng, Gong,Jing, Liping,Yu, Jian,et al. Sentiment Lexicon Construction With Hierarchical Supervision Topic Model[J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2019,27(4):704-718.
APA Deng, Gong,Jing, Liping,Yu, Jian,Sun, Shaolong,&Ng, Michael K..(2019).Sentiment Lexicon Construction With Hierarchical Supervision Topic Model.IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,27(4),704-718.
MLA Deng, Gong,et al."Sentiment Lexicon Construction With Hierarchical Supervision Topic Model".IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 27.4(2019):704-718.
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