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

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	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

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Alam, M. H., & Lee, S-G. (2012). Semantic aspect discovery for online reviews. In Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012 (pp. 816-821). [6413739] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2012.65

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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.

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