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Aspect-Specific Sentimental Word Embedding for Sentiment Analysis of Online Reviews

Published: 11 April 2016 Publication History

Abstract

Recently, Deep Convolutional Neural Networks (CNNs) have been widely applied to sentiment analysis of short texts. Naturally, word embedding techniques are used to learn continuous word representations for constructing sentence matrix as input to CNN. As for sentiment analysis of customer reviews, we argue that it is problematic to learn a single representation for a word while ignoring sentiment information and the discussed aspects. In this poster, we propose a novel word embedding model to learn sentimental word embedding given specific aspects by modeling both sentiment and syntactic context under the specific aspects. We apply our method as input to CNN for sentiment analysis in multiple domains. Experiments show that the CNN based on the proposed model can consistently achieve superior performance compared to CNN based on traditional word embedding method.

References

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Nal Kalchbrenner, Edward Grefenstette and Phil Blunsom. 2014. A Convolutional Neural Network for Modelling Sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.
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Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining (WSDM '11).
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Yang Liu, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun.2015. Topical Word Embeddings. In AAAI'15.
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J. McAuley, R. Pandey, J. Leskovec.2015. Inferring networks of substitutable and complementary products. Knowledge Discovery and Data Mining, 2015
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J. McAuley, C. Targett, J. Shi, A. van den Hengel.2015. Image-based recommendations on styles and substitutes. In SIGIR '15.
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T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS'13.
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Aliaksei Severyn and Alessandro Moschitti. 2015. Twitter Sentiment Analysis with Deep Convolutional Neural Networks. In SIGIR '15.

Cited By

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  • (2024)Aspect-based sentiment analysis: approaches, applications, challenges and trendsKnowledge and Information Systems10.1007/s10115-024-02200-966:12(7261-7303)Online publication date: 1-Dec-2024
  • (2023)FastText Word Embedding Model in Aspect-Level Sentiment Analysis of Airline Customer Reviews for Agglutinative Languages: A Case Study for Turkish4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering10.1007/978-3-031-31956-3_59(691-702)Online publication date: 27-May-2023
  • (2022)Roman Urdu Sentiment Analysis Using Transfer LearningApplied Sciences10.3390/app12201034412:20(10344)Online publication date: 14-Oct-2022
  • Show More Cited By

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Information

Published In

cover image ACM Other conferences
WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web
April 2016
1094 pages
ISBN:9781450341448
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • IW3C2: International World Wide Web Conference Committee

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Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. cnn
  2. online reviews
  3. sentiment analysis
  4. word embedding

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  • Poster

Funding Sources

  • National Natural Science Foundation of China
  • Key Research Program of the Chinese Academy of Sciences
  • 973 Program of China
  • 863 Program of China

Conference

WWW '16
Sponsor:
  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

Acceptance Rates

WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2024)Aspect-based sentiment analysis: approaches, applications, challenges and trendsKnowledge and Information Systems10.1007/s10115-024-02200-966:12(7261-7303)Online publication date: 1-Dec-2024
  • (2023)FastText Word Embedding Model in Aspect-Level Sentiment Analysis of Airline Customer Reviews for Agglutinative Languages: A Case Study for Turkish4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering10.1007/978-3-031-31956-3_59(691-702)Online publication date: 27-May-2023
  • (2022)Roman Urdu Sentiment Analysis Using Transfer LearningApplied Sciences10.3390/app12201034412:20(10344)Online publication date: 14-Oct-2022
  • (2021)Sentiment Analysis of Persian Movie Reviews Using Deep LearningEntropy10.3390/e2305059623:5(596)Online publication date: 12-May-2021
  • (2021)Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment AnalysisInternational Journal of Neural Systems10.1142/S012906572150013131:10(2150013)Online publication date: 10-Feb-2021
  • (2021)Aspect-based sentiment analysis for online reviews with hybrid attention networksWorld Wide Web10.1007/s11280-021-00898-zOnline publication date: 2-Jun-2021
  • (2020)Sentiment analysis with deep neural networks: comparative study and performance assessmentArtificial Intelligence Review10.1007/s10462-020-09845-2Online publication date: 22-May-2020
  • (2019)Method of Feature Reduction in Short Text Classification Based on Feature ClusteringApplied Sciences10.3390/app90815789:8(1578)Online publication date: 16-Apr-2019
  • (2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
  • (2019)Deep Learning for Aspect-Based Sentiment Analysis: A Comparative ReviewExpert Systems with Applications10.1016/j.eswa.2018.10.003118(272-299)Online publication date: Mar-2019
  • Show More Cited By

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