Article

Free access

Extracting product features and opinions from reviews

Authors:

Ana-Maria Popescu,

Oren EtzioniAuthors Info & Claims

HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

Pages 339 - 346

https://doi.org/10.3115/1220575.1220618

Published: 06 October 2005 Publication History

Abstract

Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.Compared to previous work, Opine achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. Opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.

References

[1]

A. Almuhareb and M. Poesio. 2004. Attribute-based and value-based clustering: An evaluation. In EMNLP, pages 158--165.]]

[2]

M. Berland and E. Charniak. 1999. Finding parts in very large corpora. In ACL, pages 57--64.]]

Digital Library

[3]

O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. 2005. Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence, 165(1):91--134.]]

[4]

V. Hatzivassiloglou and K. McKeown. 1997. Predicting the semantic orientation of adjectives. In ACL/EACL, pages 174--181.]]

Digital Library

[5]

M. Hu and B. Liu. 2004. Mining and Summarizing Customer Reviews. In KDD, pages 168--177, Seattle, WA.]]

Digital Library

[6]

R. A. Hummel and S. W. Zucker. 1983. On the foundations of relaxation labeling processes. In PAMI, pages 267--287.]]

[7]

S. Kim and E. Hovy. 2004. Determining the sentiment of opinions. In COLING.]]

Digital Library

[8]

N. Kobayashi, K. Inui, K. Tateishi, and T. Fukushima. 2004. Collecting Evaluative Expressions for Opinion Extraction. In IJCNLP, pages 596--605.]]

[9]

D. Kushal, S. Lawrence, and D. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In WWW.]]

Digital Library

[10]

D. Lin. 1998. Dependency-based evaluation of MINIPAR. In Workshop on Evaluation of Parsing Systems at ICLRE.]]

[11]

S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima. 2002. Mining product reputations on the web. In KDD.]]

Digital Library

[12]

Lee L. Pang, B and S. Vaithyanathan. 2002. Thumbs up? sentiment classification using machine learning techniques. In EMNLP, pages 79--86.]]

Digital Library

[13]

A. Popescu, A. Yates, and O. Etzioni. 2004. Class extraction from the World Wide Web. In AAAI-04 Workshop on Adaptive Text Extraction and Mining, pages 68--73.]]

[14]

A. Rangarajan. 2000. Self annealing and self annihilation: unifying deterministic annealing and relaxation labeling. In Pattern Recognition, 33:635--649.]]

[15]

E. Riloff, J. Wiebe, and T. Wilson. 2003. Learning Subjective Nouns Using Extraction Pattern Bootstrapping. In CoNLL, pages 25--32s.]]

Digital Library

[16]

H. Takamura, T. Inui, and M. Okumura. 2005. Extracting Semantic Orientations of Words using Spin Model. In ACL, pages 133--141.]]

Digital Library

[17]

P. D. Turney. 2001. Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In Procs. of the Twelfth European Conference on Machine Learning (ECML-2001), pages 491--502, Freiburg, Germany.]]

Digital Library

[18]

P. D. Turney. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Procs. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL'02), pages 417--424.]]

Digital Library

[19]

P. Turney. 2003. Inference of Semantic Orientation from Association. In CoRR cs. CL/0309034.]]

[20]

J. Wiebe. 2000. Learning subjective adjectives from corpora. In AAAI/IAAI, pages 735--740.]]

Digital Library

Cited By

Lin HZhu JXiang LZhai FZhou YZhang JZong C(2023)Topic-Oriented Dialogue SummarizationIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.327111831(1797-1810)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TASLP.2023.3271118
Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Chakraborty MKulkarni ALi QZhang ARangwala H(2022)Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span ExtractionProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539386(66-75)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539386
Show More Cited By

Extracting product features and opinions from reviews
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association
AICI '09: Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03

Often there are more than one topic, more than one holder and more than one sentiment in an opinion sentence. But currently the studies on sentiment analysis are unable to identify exactly which sentiments a holder expresses on a given topic, so these ...
A Categorized Sentiment Analysis of Chinese Reviews by Mining Dependency in Product Features and Opinions from Blogs
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01

In the past, there have been many documents focusing on English reviews for sentiment analysis. These contain abundant research results which extract features and opinions, identify semantic orientation, and associate features with opinions. Although ...
Retrieving Product Features and Opinions from Customer Reviews

A new methodology based on language models retrieves product features and opinions from a collection of free-text customer reviews about a product or service. The proposal relies on a language-modeling framework that can be applied to reviews in any ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

October 2005

1054 pages

Conference Chair:
Raymond J. Mooney
The University of Texas at Austin

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 06 October 2005

Qualifiers

Article

Acceptance Rates

HLT '05 Paper Acceptance Rate 127 of 402 submissions, 32%;

Overall Acceptance Rate 240 of 768 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

333
Total Citations
View Citations
5,102
Total Downloads

Downloads (Last 12 months)101
Downloads (Last 6 weeks)20

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lin HZhu JXiang LZhai FZhou YZhang JZong C(2023)Topic-Oriented Dialogue SummarizationIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.327111831(1797-1810)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TASLP.2023.3271118
Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Chakraborty MKulkarni ALi QZhang ARangwala H(2022)Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span ExtractionProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539386(66-75)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539386
Chen ZQian T(2022)Retrieve-and-Edit Domain Adaptation for End2End Aspect Based Sentiment AnalysisIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2022.314605230(659-672)Online publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1109/TASLP.2022.3146052
Karaoğlan KFındık O(2022)Extended rule-based opinion target extraction with a novel text pre-processing method and ensemble learningApplied Soft Computing10.1016/j.asoc.2022.108524118:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108524
Tang BMouratidis KHan MLi GLi ZIdreos SSrivastava D(2021)On m-Impact Regions and Standing Top-k Influence ProblemsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452832(1784-1796)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452832
Wang WPan S(2020)Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion ExtractionComputational Linguistics10.1162/coli_a_0036245:4(705-736)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1162/coli_a_00362
Novgorodov SGuy IElad GRadinsky K(2020)Descriptions from the CustomersACM Transactions on Internet Technology10.1145/341820220:4(1-31)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1145/3418202
Wang WPan S(2019)Transferable interactive memory network for domain adaptation in fine-grained opinion extractionProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33017192(7192-7199)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33017192
Hu LFang BHu XTavares JXu Z(2019)Appraisal expression extraction based on semantic and dependency parsingProceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing10.1145/3371425.3371645(1-8)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3371425.3371645
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents