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 Zhen Hai

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Bibliometrics: publication history
Average citations per article4.83
Citation Count29
Publication count6
Publication years2011-2017
Available for download3
Average downloads per article509.67
Downloads (cumulative)1,529
Downloads (12 Months)166
Downloads (6 Weeks)17
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6 results found Export Results: bibtexendnoteacmrefcsv

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1
June 2017 IEEE Transactions on Knowledge and Data Engineering: Volume 29 Issue 6, June 2017
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 0

In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the ...

2 published by ACM
March 2015 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Visual Understanding with RGB-D Sensors: Volume 6 Issue 2, May 2015
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 7,   Downloads (12 Months): 53,   Downloads (Overall): 307

Full text available: PDFPDF
Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies ...
Keywords: association, feature, opinion word, Opinion mining, implicit feature

3 published by ACM
July 2014 SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 8,   Downloads (12 Months): 73,   Downloads (Overall): 632

Full text available: PDFPDF
Online reviews are immensely valuable for customers to make informed purchase decisions and for businesses to improve the quality of their products and services. However, customer reviews grow exponentially while varying greatly in quality. It is generally very tedious and difficult, if not impossible, for users to read though the ...
Keywords: review selection, supervised joint topic model, review helpfulness, sentiment analysis

4
March 2014 IEEE Transactions on Knowledge and Data Engineering: Volume 26 Issue 3, March 2014
Publisher: IEEE Educational Activities Department
Bibliometrics:
Citation Count: 11

The vast majority of existing approaches to opinion feature extraction rely on mining patterns only from a single review corpus, ignoring the nontrivial disparities in word distributional characteristics of opinion features across different corpora. In this paper, we propose a novel method to identify opinion features from online reviews by ...
Keywords: Information search and retrieval, natural language processing, opinion mining, opinion feature, Chinese

5 published by ACM
October 2012 CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 2,   Downloads (12 Months): 40,   Downloads (Overall): 590

Full text available: PDFPDF
Feature-based opinion analysis has attracted extensive attention recently. Identifying features associated with opinions expressed in reviews is essential for fine-grained opinion mining. One approach is to exploit the dependency relations that occur naturally between features and opinion words, and among features (or opinion words) themselves. In this paper, we propose ...
Keywords: association, feature, sentiment analysis, seed, bootstrapping, aspect, opinion mining

6
February 2011 CICLing'11: Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 10

In sentiment analysis, identifying features associated with an opinion can help produce a finer-grained understanding of online reviews. The vast majority of existing approaches focus on explicit feature identification, few attempts have been made to identify implicit features in reviews. In this paper, we propose a novel two-phase co-occurrence association ...
Keywords: co-occurrence, opinion word, implicit feature, association rule, opinion mining



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