research-article

Visualizing Reviews Summaries as a Tool for Restaurants Recommendation

Authors:

Osnat MokrynAuthors Info & Claims

IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

Pages 607 - 616

https://doi.org/10.1145/3172944.3172947

Published: 05 March 2018 Publication History

Abstract

Online customers opinions about products and services, in the form of reviews, are a major part of today's web culture. However, customers, when looking for a product or service, do not have the time or the desire to read even a small part of the available product reviews (which themselves may be lengthy and not easy to read). Moreover, they often would like to examine reviews of similar products, and get a comprehensive picture of how different aspects of these products compare. In this work, by introducing a generic framework for analyzing and presenting a visual summary based on comparative sentences extracted from customer reviews, we offer the user an easy and intuitive understanding of the differences between a set of products. The contribution of this study is twofold: First, it focuses on reviews of intangible services (using the restaurant domain as a case study), unlike most of the related studies that consider physical products. Second, it combines state-of-the-art text analysis techniques with an intuitive visualization into an easy to use prototype to visualize summarized service comparisons to the users.

References

[1]

Arora, S., Joshi, M., & Rosé, C. P. (2009). Identifying types of claims in online customer reviews. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, 37--40. ACL.

Digital Library

[2]

Bai, X., White, D., & Sundaram, D. (2011). Purposeful visualization. In System Sciences (HICSS), 2011 44th Hawaii International Conference on (pp. 1--10). IEEE.

Digital Library

[3]

Brooke, J. (1996). SUS-A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4--7.

[4]

Cruz, F. L., Troyano, J. A., Enríquez, F., Ortega, F. J., & Vallejo, C. G. (2013). 'Long autonomy or long delay'' The importance of domain in opinion mining. Expert Systems with Applications, 40(8), 3174--3184.'

Digital Library

[5]

Removed for annoymization

[6]

Esuli, A., & Sebastiani, F. (2006). Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of Language Resources and Evaluation Conference, Vol. 6, 417--422.

[7]

Fei, Z., Huang, X., & Wu, L. (2006). Mining the relation between sentiment expression and target using dependency of words. In Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation (PACLIC20), Wuhan, China, 257--264.

[8]

Freitas, C. M., Luzzardi, P. R., Cava, R. A., Winckler, M., Pimenta, M. S., & Nedel, L. P. (2002). On evaluating information visualization techniques. In Proceedings of the Working Conference on Advanced Visual Interfaces, 373--374. ACM.

Digital Library

[9]

Gamon, M., Aue, A., Corston-Oliver, S., & Ringger, E. (2005). Pulse: Mining customer opinions from free text. In Advances in Intelligent Data Analysis VI, 121--132. Springer Berlin Heidelberg.

Digital Library

[10]

Gu, Y. H., & Yoo, S. J. (2010). Searching a best product based on mining comparison sentences. In SCIS & ISIS SCIS & ISIS 2010, 929--933. Japan Society for Fuzzy Theory and Intelligent Informatics.

[11]

He, S., Yuan, F., & Wang, Y. (2012, April). Extracting the comparative relations for mobile reviews. In Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on, 3247--3250). IEEE.

[12]

Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (168--177). ACM.

Digital Library

[13]

Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201--214.

Digital Library

[14]

Jindal, N., & Liu, B. (2006b). Identifying comparative sentences in text documents. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 244--251). ACM.

Digital Library

[15]

Jindal, N., & Liu, B. (2006a). Mining comparative sentences and relations. In Association for the Advancement of Artificial Intelligence, Vol. 22, 1331--1336.

Digital Library

[16]

Kessler, J. S., Eckert, M., Clark, L., & Nicolov, N. (2010). The ICWSM 2010 JDPA sentiment corpus for the automotive domain. In 4th International AAAI Conf. on Weblogs and Social Media Data Workshop Challenge (ICWSM-DWC), Washington, DC.

[17]

Kessler, W., & Kuhn, J. (2013). Detection of product comparisons-how far does an out-of-the-box semantic role labeling system take you. In EMNLP 1892--1897.

[18]

Khan, M., & Khan, S. S. (2011). Data and information visualization methods, and interactive mechanisms: A survey. International Journal of Computer Applications, 34(1), 1--14.

[19]

Lafferty, J., McCallum, A., & Pereira, F. C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001) 31--36. ACM.

Digital Library

[20]

Liu, B. (2007). Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media.

Digital Library

[21]

Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415--463). Springer US.

[22]

Lovelock, C. H. (1983). Classifying services to gain strategic marketing insights. Journal of Marketing, 9--20.

[23]

Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. In ACL (System Demonstrations) 55--60.

[24]

Marrese-Taylor, E., Velásquez, J. D., & Bravo-Marquez, F. (2014). A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Systems with Applications, 41(17), 7764--7775.

Digital Library

[25]

Mayhew, D. J. (1991). Principles and guidelines in software user interface design. Prentice-Hall, Inc.

Digital Library

[26]

Miao, Q., Li, Q., & Dai, R. (2009). AMAZING: A sentiment mining and retrieval system. Expert Systems with Applications, 36(3), 7192--7198.

Digital Library

[27]

Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39--41.

Digital Library

[28]

Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185--200.

Digital Library

[29]

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1--2), 1--135.

Digital Library

[30]

Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14--46.

Digital Library

[31]

Saad Andaleeb, S., & Conway, C. (2006). Customer satisfaction in the restaurant industry: an examination of the transaction-specific model. Journal of Services Marketing, 20(1), 3--11.

[32]

Sha, F., & Pereira, F. (2003). Shallow parsing with conditional random fields. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Vol. 1, 134--141). ACL.

Digital Library

[33]

Shamim, A., Balakrishnan, V., & Tahir, M. (2014). Evaluation of opinion visualization techniques. Information Visualization, 147387/1614550537.

[34]

Smith, S. L., & Mosier, J. N. (1984). Design guidelines for user-system interface software (No. MTR-9420). MITRE CORP BEDFORD MA.

[35]

Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. In Australasian Joint Conference on Artificial Intelligence, 1015--1021. Springer Berlin Heidelberg.

Digital Library

[36]

Stanford Natural Language Processing Group, (2009). The Stanford Parser: A statistical parser, http://nlp.stanford.edu/software/lex-parser.shtml.

[37]

Sutton, C., & McCallum, A. (2006). An introduction to conditional random fields for relational learning. Introduction to statistical relational learning, 93--128.

[38]

Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 417--424. ACL.

Digital Library

[39]

Von Alan, R. H., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75--105.

Digital Library

[40]

Wang, W., Zhao, T., Xin, G., & Xu, Y. (2014). Recognizing Comparative Sentences from Chinese Review Texts. International Journal of Database Theory and Application, 7(5), 29--38.

[41]

Ware, C. (2012). Information visualization: Perception for design. Elsevier

Digital Library

[42]

Xu, K., Liao, S. S., Li, J., & Song, Y. (2011). Mining comparative opinions from customer reviews for Competitive Intelligence. Decision support systems, 50(4), 743--754.

Digital Library

[43]

Ying, D., & Jiang, J. (2015). Towards opinion summarization from online forums. ACL.

[44]

Zhang, K., Narayanan, R., & Choudhary, A. (2009). Mining online customer reviews for ranking products. Technical Report, EECS Dept, Northwestern Univ.

[45]

Zhang, K., Narayanan, R., & Choudhary, A. (2010, June). Voice of the customers: Mining online customer reviews for product feature-based ranking. In Proceedings of the 3rd Conference on Online Social Networks, 11.

Digital Library

[46]

Zhang, L., & Liu, B. (2014). Aspect and entity extraction for opinion mining. In Data Mining and Knowledge Discovery for Big Data, 1--40. Springer Berlin Heidelberg.

Cited By

Warner JPavel ANguyen TAgrawala MHartmann B(2023)SlideSpecs: Automatic and Interactive Presentation Feedback CollationProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584035(695-709)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584035
Park SChae DBae HPark SKim SSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable RecommendationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498515(784-793)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498515
Chehal DGupta PGulati P(2020)RETRACTED ARTICLE: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendationsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-01956-612:5(5055-5070)Online publication date: 16-Apr-2020
https://doi.org/10.1007/s12652-020-01956-6
Show More Cited By

Index Terms

Recommendations

Analyzing and visualizing comprehensive and personalized online product reviews
Abstract
This paper presents a method of analyzing and visualizing e-commerce sites’ product reviews considering customers’ characteristics. Typically, product review data are unstructured and have no fixed format. Such data can be considered as valuable ...
Visualizing Süleymaname: Analyzing and Visualizing Embedded Spatiotemporal Information in a 16th Century Illustrated Manuscript
IV '15: Proceedings of the 2015 19th International Conference on Information Visualisation

In this paper, we present the analysis and the visualization process of spatiotemporal information in a 16th century illustrated manuscript. The purpose of this research is to find alternative ways of exploring the illustrations of a 16th century ...
Visualizing Business Data with Generalized Treemaps

Business data is often presented using simple business graphics.These familiar visualizations are effective for providing overviews,but fall short for the presentation of large amounts of detailedinformation. Treemaps can provide such detail, but are ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

March 2018

698 pages

ISBN:9781450349451

DOI:10.1145/3172944

General Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Yoshinori Hijikata
Kwansei Gakuin University, Japan
,
Jun Rekimoto
University of Tokyo, Japan
,
Program Chairs:
Margaret Burnett
Oregon State University, USA
,
Mark Billinghurst
University of South Australia, Australia
,
Aaron Quigley
University of St Andrews, UK

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI'18

Sponsor:

SIGAI

IUI'18: 23rd International Conference on Intelligent User Interfaces

March 7 - 11, 2018

Tokyo, Japan

Acceptance Rates

IUI '18 Paper Acceptance Rate 43 of 299 submissions, 14%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
501
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Warner JPavel ANguyen TAgrawala MHartmann B(2023)SlideSpecs: Automatic and Interactive Presentation Feedback CollationProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584035(695-709)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584035
Park SChae DBae HPark SKim SSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable RecommendationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498515(784-793)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498515
Chehal DGupta PGulati P(2020)RETRACTED ARTICLE: Implementation and comparison of topic modeling techniques based on user reviews in e-commerce recommendationsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-01956-612:5(5055-5070)Online publication date: 16-Apr-2020
https://doi.org/10.1007/s12652-020-01956-6
Watson KSohn SSchriber SGross MMuniz CKapadia MFu WPan SBrdiczka OChau PCalvary G(2019)StoryPrintProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302302(303-311)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3302302

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten