ML Anthology

Topic Extraction from Online Reviews for Classification and Recommendation

Ruihai Dong, Markus Schaal, Michael P. O'Mahony, Barry Smyth
IJCAI 2013 pp. 1310-1316
/ijcai/2013/dong2013ijcai-topic/

Abstract

Automatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information, mined from a review corpus.

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Cite

Text

Dong et al. "Topic Extraction from Online Reviews for Classification and Recommendation." International Joint Conference on Artificial Intelligence, 2013.

Markdown

[Dong et al. "Topic Extraction from Online Reviews for Classification and Recommendation." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/dong2013ijcai-topic/)

BibTeX

@inproceedings{dong2013ijcai-topic,
  title     = {{Topic Extraction from Online Reviews for Classification and Recommendation}},
  author    = {Dong, Ruihai and Schaal, Markus and O'Mahony, Michael P. and Smyth, Barry},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {1310-1316},
  url       = {https://mlanthology.org/ijcai/2013/dong2013ijcai-topic/}
}