Semantic aspect discovery for online reviews

Md Hijbul Alam; Sang-Geun Lee

doi:10.1109/ICDM.2012.65

Semantic aspect discovery for online reviews

Md Hijbul Alam, Sang-Geun Lee

Center for Artificial Intelligence Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

The number of opinions and reviews about different products and services is growing online. Users frequently look for important aspects of a product or service in the reviews. Usually, they are interested in semantic (i.e., sentiment-oriented) aspects. However, extracting semantic aspects with supervised methods is very expensive. We propose a domain independent unsupervised model to extract semantic aspects, and conduct qualitative and quantitative experiments to evaluate the extracted aspects. The experiments show that our model effectively extracts semantic aspects with correlated top words. In addition, the conducted evaluation on aspect sentiment classification shows that our model outperforms other models by 5-7% in terms of macro-average F1.

Original language	English
Title of host publication	Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages	816-821
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2012.65
Publication status	Published - 2012
Event	12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium Duration: 2012 Dec 10 → 2012 Dec 13

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	12th IEEE International Conference on Data Mining, ICDM 2012
Country/Territory	Belgium
City	Brussels
Period	12/12/10 → 12/12/13

Keywords

Aspect discovery
Opinion mining
Sentiment analysis
Topic model

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/ICDM.2012.65

Cite this

@inproceedings{e8caeddc5f3842fab14d4670011d46c5,

title = "Semantic aspect discovery for online reviews",

abstract = "The number of opinions and reviews about different products and services is growing online. Users frequently look for important aspects of a product or service in the reviews. Usually, they are interested in semantic (i.e., sentiment-oriented) aspects. However, extracting semantic aspects with supervised methods is very expensive. We propose a domain independent unsupervised model to extract semantic aspects, and conduct qualitative and quantitative experiments to evaluate the extracted aspects. The experiments show that our model effectively extracts semantic aspects with correlated top words. In addition, the conducted evaluation on aspect sentiment classification shows that our model outperforms other models by 5-7% in terms of macro-average F1.",

keywords = "Aspect discovery, Opinion mining, Sentiment analysis, Topic model",

author = "Alam, {Md Hijbul} and Sang-Geun Lee",

year = "2012",

doi = "10.1109/ICDM.2012.65",

language = "English",

isbn = "9780769549057",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "816--821",

booktitle = "Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012",

note = "12th IEEE International Conference on Data Mining, ICDM 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

}

TY - GEN

T1 - Semantic aspect discovery for online reviews

AU - Alam, Md Hijbul

AU - Lee, Sang-Geun

PY - 2012

Y1 - 2012

N2 - The number of opinions and reviews about different products and services is growing online. Users frequently look for important aspects of a product or service in the reviews. Usually, they are interested in semantic (i.e., sentiment-oriented) aspects. However, extracting semantic aspects with supervised methods is very expensive. We propose a domain independent unsupervised model to extract semantic aspects, and conduct qualitative and quantitative experiments to evaluate the extracted aspects. The experiments show that our model effectively extracts semantic aspects with correlated top words. In addition, the conducted evaluation on aspect sentiment classification shows that our model outperforms other models by 5-7% in terms of macro-average F1.

AB - The number of opinions and reviews about different products and services is growing online. Users frequently look for important aspects of a product or service in the reviews. Usually, they are interested in semantic (i.e., sentiment-oriented) aspects. However, extracting semantic aspects with supervised methods is very expensive. We propose a domain independent unsupervised model to extract semantic aspects, and conduct qualitative and quantitative experiments to evaluate the extracted aspects. The experiments show that our model effectively extracts semantic aspects with correlated top words. In addition, the conducted evaluation on aspect sentiment classification shows that our model outperforms other models by 5-7% in terms of macro-average F1.

KW - Aspect discovery

KW - Opinion mining

KW - Sentiment analysis

KW - Topic model

UR - http://www.scopus.com/inward/record.url?scp=84874104662&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874104662&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2012.65

DO - 10.1109/ICDM.2012.65

M3 - Conference contribution

AN - SCOPUS:84874104662

SN - 9780769549057

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 816

EP - 821

BT - Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012

T2 - 12th IEEE International Conference on Data Mining, ICDM 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

Semantic aspect discovery for online reviews

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this