poster

Topic and keyword re-ranking for LDA-based topic modeling

Authors:

Weihong QianAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1757 - 1760

https://doi.org/10.1145/1645953.1646223

Published: 02 November 2009 Publication History

Get Access

Abstract

Topic-based text summaries promise to help average users quickly understand a text collection and derive insights. Recent research has shown that the Latent Dirichlet Allocation (LDA) model is one of the most effective approaches to topic analysis. However, the LDA-based results may not be ideal for human understanding and consumption. In this paper, we present several topic and keyword re-ranking approaches that can help users better understand and consume the LDA-derived topics in their text analysis. Our methods process the LDA output based on a set of criteria that model a user's information needs. Our evaluation demonstrates the usefulness of the methods in summarizing several large-scale, real world data sets.

References

[1]

C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2006.

Digital Library

Google Scholar

[2]

D. Blei and J. Lafferty. Topic Models, chapter: Topic Models. Taylor and Francis, 2009. (in Press).

Google Scholar

[3]

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

Google Scholar

[4]

S. Deerwester, S. Dumais, T. Landauer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391--407, 1990.

Crossref

Google Scholar

[5]

T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101:5228--5235, 2004.

Crossref

Google Scholar

[6]

X. He, D. Cai, and P. Niyogi. Laplacian score for feature selection. In Proceedings of NIPS, 2005.

Google Scholar

[7]

T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of UAI, pages 289--296, 1999.

Digital Library

Google Scholar

[8]

M. Lan, C. Tan, H. Low, and S. Sung. A comprehensive comparative study on term weighting schemes for text categorization with support vector machines. In WWW (Special interest tracks and posters), pages 1032--1033, 2005.

Digital Library

Google Scholar

[9]

S. Liu, M. X. Zhou, S. Pan, W. Qian, W. Cai, and X. Lian. Interactive, topics-based visual text summarization and analysis. In Proceedings of the CIKM, 2009.

Digital Library

Google Scholar

[10]

A. K. McCallum. Mallet: A machine learning for language toolkit. 2002.

Google Scholar

[11]

P. Mitra, C. Murthy, and S. Pal. Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(3):301--312, 2002.

Digital Library

Google Scholar

[12]

M. Sahami. Using Machine Learning to Improve Information Access. PhD thesis, Department of Computer Science, Stanford University, USA, 1998.

Digital Library

Google Scholar

[13]

G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523, 1988.

Digital Library

Google Scholar

[14]

Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of ICML, pages 412--420, 1997.

Digital Library

Google Scholar

Cited By

View all

Yang YLalor JAbbasi AZeng D(2025)Hierarchical Deep Document ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348752337:1(351-364)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3487523
Verbeij TBeyens ITrilling DValkenburg P(2024)Happiness and Sadness in Adolescents’ Instagram Direct Messaging: A Neural Topic Modeling ApproachSocial Media + Society10.1177/2056305124122965510:1Online publication date: 23-Feb-2024
https://doi.org/10.1177/20563051241229655
Du JZhao HYe ZLi M(2024)Bert-BiLSTM Model for Sentiment Analysis Using Contextual Embeddings and Bidirectional Dependencies2024 International Symposium on Internet of Things and Smart Cities (ISITSC)10.1109/ISITSC64373.2024.00022(88-93)Online publication date: 21-Jun-2024
https://doi.org/10.1109/ISITSC64373.2024.00022
Show More Cited By

Index Terms

Topic and keyword re-ranking for LDA-based topic modeling
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation

Recommendations

Research on Multi-document Summarization Based on LDA Topic Model
IHMSC '14: Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02

Compared with VSM (Vector Space Model) and graph-ranking models, LDA (Latent Dirichlet Allocation) Model can discover latent topics in the corpus and latent topics are beneficial to use sentence-ranking mechanisms to form a good summary. In the paper, ...
Topic-driven reader comments summarization
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Readers of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking ...
Topic sentiment change analysis
MLDM'11: Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition

Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the ...

Comments

Information & Contributors

Information

Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM '09

Sponsor:

CIKM '09: Conference on Information and Knowledge Management

November 2 - 6, 2009

Hong Kong, China

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

56
Total Citations
View Citations
1,632
Total Downloads

Downloads (Last 12 months)120
Downloads (Last 6 weeks)16

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Yang YLalor JAbbasi AZeng D(2025)Hierarchical Deep Document ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348752337:1(351-364)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3487523
Verbeij TBeyens ITrilling DValkenburg P(2024)Happiness and Sadness in Adolescents’ Instagram Direct Messaging: A Neural Topic Modeling ApproachSocial Media + Society10.1177/2056305124122965510:1Online publication date: 23-Feb-2024
https://doi.org/10.1177/20563051241229655
Du JZhao HYe ZLi M(2024)Bert-BiLSTM Model for Sentiment Analysis Using Contextual Embeddings and Bidirectional Dependencies2024 International Symposium on Internet of Things and Smart Cities (ISITSC)10.1109/ISITSC64373.2024.00022(88-93)Online publication date: 21-Jun-2024
https://doi.org/10.1109/ISITSC64373.2024.00022
Shivanadhuni SPrakash R(2024)A Review: Comprehensive study on societal Analysis for health care system Using topic modeling Paradigms2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC60049.2024.10507910(1-5)Online publication date: 27-Jan-2024
https://doi.org/10.1109/ASSIC60049.2024.10507910
Yan XZhang YZhang C(2024)Utilizing cognitive signals generated during human reading to enhance keyphrase extraction from microblogsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10361461:2Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103614
Fiorin ALópez Pablo CLejeune MHamza Siraj ADella Mea V(2024)Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte DatasetsJournal of Imaging Informatics in Medicine10.1007/s10278-024-01043-837:6(2996-3008)Online publication date: 28-May-2024
https://doi.org/10.1007/s10278-024-01043-8
Zoutsou KSfakakis MPapachristopoulos LPapatheodorou C(2024)Automated Topic Exploration in a Cultural Heritage CorpusMetadata and Semantic Research10.1007/978-3-031-65990-4_21(229-240)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-3-031-65990-4_21
Rajapaksha DAhangama SDushanthi MMadhurangi K(2023)Analyzing Trends and Topics of Sinhala Hate Speech on Twitter: A Time Series Approach2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS)10.1109/ICIIS58898.2023.10253497(67-72)Online publication date: 25-Aug-2023
https://doi.org/10.1109/ICIIS58898.2023.10253497
Srinivas SRamachandiran S(2023)Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviewsAnnals of Operations Research10.1007/s10479-022-05162-9333:2-3(1045-1075)Online publication date: 13-Jan-2023
https://doi.org/10.1007/s10479-022-05162-9
Kim TYang G(2022)Predicting Duplicate in Bug Report Using Topic-Based Duplicate Learning With Fine Tuning-Based BERT AlgorithmIEEE Access10.1109/ACCESS.2022.322623810(129666-129675)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3226238
Show More Cited By

View Options

Login options

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

Cited By

Index Terms

Recommendations

Research on Multi-document Summarization Based on LDA Topic Model

Topic-driven reader comments summarization

Topic sentiment change analysis

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Research on Multi-document Summarization Based on LDA Topic Model

Topic-driven reader comments summarization

Topic sentiment change analysis

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations