The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Aspect and sentiment unification model for online review analysis
Abstract
User-generated reviews on the Web contain sentiments about detailed aspects of products and services. However, most of the reviews are plain text and thus require much effort to obtain information about relevant details. In this paper, we tackle the problem of automatically discovering what aspects are evaluated in reviews and how sentiments for different aspects are expressed. We first propose Sentence-LDA (SLDA), a probabilistic generative model that assumes all words in a single sentence are generated from one aspect. We then extend SLDA to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects. ASUM discovers pairs of {aspect, sentiment} which we call senti-aspects. We applied SLDA and ASUM to reviews of electronic devices and restaurants. The results show that the aspects discovered by SLDA match evaluative details of the reviews, and the senti-aspects found by ASUM capture important aspects that are closely coupled with a sentiment. The results of sentiment classification show that ASUM outperforms other generative models and comes close to supervised classification methods. One important advantage of ASUM is that it does not require any sentiment labels of the reviews, which are often expensive to obtain.
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Cites
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Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
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Probabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
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Extracting product features and opinions from reviews
Popescu, A.-M., & Etzioni, O. (2005) · Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05 · doi:10.3115/1220575.1220618 · Cited by 1,698
Measuring praise and criticism: Inference of semantic orientation from association
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Topic modeling: beyond bag-of-words
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Joint sentiment/topic model for sentiment analysis
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Topic sentiment mixture: modeling facets and opinions in weblogs
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Cited by
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02145-9 · Cited by 3,281
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,260
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,306
Survey on Aspect-Level Sentiment Analysis
Schouten, K., & Frasincar, F. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2485209 · Cited by 832
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2023) · Encyclopedia of Machine Learning and Data Science · doi:10.1007/978-1-4899-7502-7_907-2 · Cited by 518
Metadata
- Authors
- Jo, Y., & Oh, A. H.
- Venue
- Proceedings of the fourth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2011-02-09
- Pages
- 815-824
- OpenAlex
- W2044429219
- Cited by
- 767
- References
- 25
- FWCI
- 36.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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