More Web Proxy on the site http://driver.im/

research-article

Opinion Mining with Interpretable Random Forests

Authors:

Phuc Quang Tran,

Hanh Thi My Le,

Hiep Xuan HuynhAuthors Info & Claims

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

Pages 27 - 32

https://doi.org/10.1145/3591569.3591595

Published: 13 July 2023 Publication History

Abstract

This paper proposes an interpretable random forest for opinion mining on hotel reviews. This model performs the task of sentiment polarity about hotel as positive or negative. In addition, we constructed the criteria importance measures to explain and clarify the relationship and interactions of the hotel name, hotel aspect, hotel reviewer, and reviewer time affects the orientation of sentiment is negative or positive. An interpretable random forest was evaluated on three scenarios, which we built based on important features of the hotel reviews. The experimental results on the hotel reviews data set have shown the effectiveness of the proposed issues.

References

[1]

B. Liu, and L. Zhang. 2012. A survey of opinion mining and sentiment analysis. In Mining text data, Springer, 2(5), 415-463.

[2]

B. Liu. 2015. Sentiment Analysis: mining sentiments, opinions, and emotions. Cambridge University Press.2nd edition, ch.2, 16-45.

[3]

B. Pang, L. Lee, and S. Vaithyanathan.2002. Thumbs up? Sentiment Classification Using Machine Learning Techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’02), vol.10, 79-86.

[4]

Phuc Quang Tran, Hai Thanh Nguyen, Hanh My Thi Le, and Hiep Xuan Huynh. 2021. Ensemble Learning for Mining Opinions on Food Reviews. In Proceedings of the International Conference on Context-Aware Systems and Applications(ICCASA 2021), pp 56–70.

[5]

K. Dave, S. Lawrence, and D. M. Pennock. 2003. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proceedings of the 12th International Conference on World Wide Web (WWW '03), 519-528.

[6]

L. Breiman, J. H. Friedman, R. A. Olshen, (1984). “Classification and Regression Trees”. CA: Wadsworth.

[7]

B. Efron. 1979. Bootstrap methods: Another look at the jackknife. Journal of The Annals of Statistics, 7(1), 1-26.

[8]

L. Breiman. (1996).“Bagging predictors”. Machine Learning, vol. 24, pp.123-140.

[9]

L. Breiman. (2001).“Random forests”. Machine Learning, vol. 45, no.1, pp. 5-32.

Digital Library

[10]

H. Wang, Y. Lu, and C. Zhai. 2011. Latent Aspect Rating Analysis without Aspect Keyword Supervision. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), 618-626.

[11]

Phuc Quang Tran, Ngoan Thanh Trieu, Nguyen Vu Dao, Hai Thanh Nguyen and Hiep Xuan Huynh, “Effective Opinion Words Extraction for Food Reviews Classification”. International Journal of Advanced Computer Science and Applications(IJACSA),11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110755

[12]

D. Apley, an Jingyu Zhu.2019. Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 82,1-44.

[13]

Arti, K. P. Dubey, and S. Agrawal.2019. An Opinion Mining for Indian Premier League Using Machine Learning Techniques. In Proceedings of the 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU).

[14]

I. P. A. M. Utama, S. S. Prasetyowati, Y. Sibaroni. 2021. Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier”. Jurnal Media Informatika Budidarma, 5(2), 630-639.

[15]

V.B. Raut, D.D. Londhe. 2014. Opinion Mining and Summarization of Hotel Reviews. In Proceedings the Sixth International Conference on Computational Intelligence and Communication Networks, IEEE Computer Society, 556-559.

Digital Library

Cited By

Govardhanan KManikandan RArunkumar BAbirami SChandar BGopalakrishnan K(2024)Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion MiningIEEE Access10.1109/ACCESS.2024.337185712(38123-38134)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3371857

Index Terms

Opinion Mining with Interpretable Random Forests
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

Recommendations

Opinion Mining with Interpretable Random Density Forests
ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft Computing

Interpreting and explaining complex models such as ensemble machine learning models for opinion mining is essential to increase the level of transparency fairness and reliability of positive and negative opinion prediction results. Although ensemble ...
Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation
Abstract
Mandalika in the Province of West Nusa Tenggara (NTB) is one of the top 10 priority tourist destinations in Indonesia. Mandalika is also a Tourism Special Economic Zone because of its great tourism potential. The Special Tourism Economic Zone is ...
Opinion Mining and Summarization of Hotel Reviews
CICN '14: Proceedings of the 2014 International Conference on Computational Intelligence and Communication Networks

Everyday many users purchases product, book travel tickets, buy goods and services through web. Users also share their views about product, hotel, news, and topic on web in the form of reviews, blogs, comments etc. Many users read review information ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

February 2023

310 pages

ISBN:9781450399616

DOI:10.1145/3591569

Copyright © 2023 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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICIIT 2023

ICIIT 2023: 2023 8th International Conference on Intelligent Information Technology

February 24 - 26, 2023

Da Nang, Vietnam

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
44
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)3

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Govardhanan KManikandan RArunkumar BAbirami SChandar BGopalakrishnan K(2024)Parameter Reputation Model for Cloud Service Recommendation and Ranking Using Opinion MiningIEEE Access10.1109/ACCESS.2024.337185712(38123-38134)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3371857

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.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents