Semantic aspect discovery for online reviews

Md Hijbul Alam, Sang-Geun Lee

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages816-821
Number of pages6
DOIs
Publication statusPublished - 2012
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: 2012 Dec 102012 Dec 13

Publication series

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

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period12/12/1012/12/13

Keywords

  • Aspect discovery
  • Opinion mining
  • Sentiment analysis
  • Topic model

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    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