Segment-level joint topic-sentiment model for online review analysis

Qinjuan YANG; Yanghui RAO; Haoran XIE; Jiahai WANG; Fu Lee WANG; Wai Hong CHAN

doi:10.1109/MIS.2019.2899142

Segment-level joint topic-sentiment model for online review analysis

Qinjuan YANG, Yanghui RAO, Haoran XIE, Jiahai WANG, Fu Lee WANG, Wai Hong CHAN

Research output: Journal Publications › Journal Article (refereed) › peer-review

32 Citations (Scopus)

Abstract

With the rapid development of the Internet, an increasing number of users enjoy to shop online and express their reviews on the products and services. Analysis of these online reviews can not only help potential users make rational decisions when purchasing but also improves the quality of products and services. Hence, sentiment analysis for online reviews has become an important and meaningful research domain.

Original language	English
Article number	8667097
Pages (from-to)	43-50
Number of pages	8
Journal	IEEE Intelligent Systems
Volume	34
Issue number	1
DOIs	https://doi.org/10.1109/MIS.2019.2899142
Publication status	Published - Jan 2019
Externally published	Yes

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10.1109/MIS.2019.2899142

Cite this

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title = "Segment-level joint topic-sentiment model for online review analysis",

abstract = "With the rapid development of the Internet, an increasing number of users enjoy to shop online and express their reviews on the products and services. Analysis of these online reviews can not only help potential users make rational decisions when purchasing but also improves the quality of products and services. Hence, sentiment analysis for online reviews has become an important and meaningful research domain.",

author = "Qinjuan YANG and Yanghui RAO and Haoran XIE and Jiahai WANG and WANG, {Fu Lee} and CHAN, {Wai Hong}",

year = "2019",

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language = "English",

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AU - YANG, Qinjuan

AU - RAO, Yanghui

AU - XIE, Haoran

AU - WANG, Jiahai

AU - WANG, Fu Lee

AU - CHAN, Wai Hong

PY - 2019/1

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AB - With the rapid development of the Internet, an increasing number of users enjoy to shop online and express their reviews on the products and services. Analysis of these online reviews can not only help potential users make rational decisions when purchasing but also improves the quality of products and services. Hence, sentiment analysis for online reviews has become an important and meaningful research domain.

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U2 - 10.1109/MIS.2019.2899142

DO - 10.1109/MIS.2019.2899142

M3 - Journal Article (refereed)

AN - SCOPUS:85063052604

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SP - 43

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JO - IEEE Intelligent Systems and Their Applications

JF - IEEE Intelligent Systems and Their Applications

SN - 1541-1672

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Segment-level joint topic-sentiment model for online review analysis

Abstract

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Erratum : Segment-Level Joint Topic-Sentiment Model for Online Review Analysis (IEEE Intelligent Systems (2019) 34:1 (43-50) DOI: 10.1109/MIS.2019.2899142)

Cite this