research-article

Word Embedding based Clustering to Detect Topics in Social Media

Authors:

Carmela Comito,

Agostino Forestiero,

Clara PizzutiAuthors Info & Claims

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

Pages 192 - 199

https://doi.org/10.1145/3350546.3352518

Published: 14 October 2019 Publication History

Abstract

Social media are playing an increasingly important role in reporting major events happening in the world. However, detecting events and topics of interest from social media is a challenging task due to the huge magnitude of the data and the complex semantics of the language being processed. The paper proposes an online algorithm to discover topics that incrementally groups short text by incorporating the textual content with latent feature vector representations of words appearing in the text, trained on very large corpora to improve the check-in topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, the approach obtains significant improvements with respect to classical topic detection methods.

References

[1]

2017. Amazon Mechanical Turk. https://www.mturk.com

[2]

2017. Using Machine Learning to Analyze Twitter for Real Time Influenza Surveillance. https://medium.com/@justinzcai/

[3]

Luca Maria Aiello, Georgios Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Goker, Ioannis Kompatsiaris, and Alejandro Jaimes. 2013. Sensing Trending Topics in Twitter. IEEE Transactions on Multimedia 15, 6 (2013), 1268–1282.

Digital Library

[4]

Nasser Alsaedi, Pete Burnap, and Omer Rana. 2017. Can We Predict a Riot? Disruptive Event Detection Using Twitter. ACM Trans. Internet Technol. 17, 2 (March 2017), 18:1–18:26.

Digital Library

[5]

Farzindar Atefeh and Wael Khreich. 2015. A Survey of Techniques for Event Detection in Twitter. Comput. Intell. 31, 1 (Feb. 2015), 132–164.

Digital Library

[6]

Hila Becker, Mor Naaman, and Luis Gravano. 2011. Beyond Trending Topics: Real-World Event Identification on Twitter. In Proceedings of the Fifth International Conference on Weblogs and Social Media.

[7]

David M. Blei, Andrew Y. Ng, Michael I. Jordan, and John Lafferty. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003), 2003.

Digital Library

[8]

Moody Christopher. 2016. Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec. https://arxiv.org/pdf/1605.02019.pdf(2016).

[9]

Carmela Comito, Clara Pizzuti, and Nicola Procopio. 2016. Online Clustering for Topic Detection in Social Data Streams. In 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI. 362–369.

[10]

Carmela Comito, Clara Pizzuti, and Nicola Procopio. 2017. How people talk about health?: Detecting Health Topics from Twitter Streams. In Proceedings of the International Conference on Big Data and Internet of Things,. 85–90.

Digital Library

[11]

Mário Cordeiro and João Gama. 2016. Online Social Networks Event Detection: A Survey. Springer International Publishing, Cham, 1–41.

[12]

Xiangfeng Dai, Marwan Bikdash, and Bradley Meyer. 2017. From social media to public health surveillance: Word embedding based clustering method for twitter classification. In Proc. of the IEEE SoutheastCon, Charlotte NC March 30 ? April 2, 2017. 1–7.

[13]

Maha Fraj, Mohamed Aymen Ben HajKacem, and Nadia Essoussi. 2018. A Novel Tweets Clustering Method using Word Embeddings. In 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018, Aqaba, Jordan, October 28 - Nov. 1, 2018. 1–7.

[14]

Anuradha Goswami and Ajey Kumar. 2016. A survey of event detection techniques in online social networks. Social Network Analysis and Mining 6, 1 (17 Nov 2016), 107.

[15]

Mahmud Hasan, Mehmet A Orgun, and Rolf Schwitter. 2017. A survey on real-time event detection from Twitter data stream. Journal of Information Science online March 29017, https://doi.org/10.1177/0165551517698564(2017), 1–21.

[16]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In 1st International Conference on Learning Representations, ICLR 2013.

[17]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and Their Compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2(NIPS’13). 3111–3119.

Digital Library

[18]

Michael J. Paul and Mark Dredze. 2011. You Are What You Tweet: Analyzing Twitter for Public Health. In Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July 17-21, 2011.

[19]

Michael J. Paul and Mark Dredze. 2014. Discovering Health Topics in Social Media Using Topic Models. PLoS ONE 9, 8 (2014).

[20]

Saša Petrović, Miles Osborne, and Victor Lavrenko. 2010. Streaming First Story Detection with Application to Twitter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 181–189.

[21]

Cao Qimin, Guo Qiao, Wang Yongliang, and Wu Xianghua. 2015. Text Clustering Using VSM with Feature Clusters. Neural Comput. Appl. 26, 4 (May 2015), 995–1003.

Digital Library

[22]

Gerard Salton and Michael J. McGill. 1986. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA.

Digital Library

[23]

Yee Whye Teh, David Newman, and Max Welling. 2007. A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. Adv. Neural Inf. Process. Syst 19 (2007).

[24]

Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl. 2017. Survey and Experimental Analysis of Event Detection Techniques for Twitter. Computer Journal 60, 3 (2017), 329–346.

[25]

Yiming Yang, Tom Pierce, and Jaime Carbonell. 1998. A Study of Retrospective and On-line Event Detection. In Proc. of the 21st ACM SIGIR(SIGIR ’98). ACM, 28–36.

Digital Library

[26]

Jie Yin, Andrew Lampert, Mark A. Cameron, Bella Robinson, and Robert Power. 2012. Using Social Media to Enhance Emergency Situation Awareness. IEEE Intelligent Systems 27, 6 (2012), 52–59.

Digital Library

Cited By

Ojha NVaish A(2024)Hate Speech in Indian Cyber Space at the Intersection of Law and TechnologyIntersections Between Rights and Technology10.4018/979-8-3693-1127-1.ch009(156-191)Online publication date: 10-Jul-2024
https://doi.org/10.4018/979-8-3693-1127-1.ch009
Briciu ACălin AMiholca DMoroz-Dubenco CPetrașcu VDascălu G(2024)Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian ReviewsMathematics10.3390/math1203045612:3(456)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030456
Alsmadi MAlzaqebah MJawarneh SALmarashdeh IAl-Betar MAlwohaibi MAl-Mulla NAhmed EAL Smadi A(2024)Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clusteringJournal of Big Data10.1186/s40537-024-00930-911:1Online publication date: 9-May-2024
https://doi.org/10.1186/s40537-024-00930-9
Show More Cited By

Index Terms

Word Embedding based Clustering to Detect Topics in Social Media
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

Recommendations

Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog

As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...
Sentiment Analysis Using Word Polarity of Social Media

Sentiment analysis requires a sentiment dictionary that maps words to sentiments. Further, sentiment weight is an important subtopic in the measurement of the strength of sentiments. A sentiment is the emotional response of an individual toward an ...
Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis
Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers

A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 2019

507 pages

ISBN:9781450369343

DOI:10.1145/3350546

Copyright © 2019 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WI '19

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

Acceptance Rates

Overall Acceptance Rate 118 of 178 submissions, 66%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

35
Total Citations
View Citations
409
Total Downloads

Downloads (Last 12 months)96
Downloads (Last 6 weeks)11

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ojha NVaish A(2024)Hate Speech in Indian Cyber Space at the Intersection of Law and TechnologyIntersections Between Rights and Technology10.4018/979-8-3693-1127-1.ch009(156-191)Online publication date: 10-Jul-2024
https://doi.org/10.4018/979-8-3693-1127-1.ch009
Briciu ACălin AMiholca DMoroz-Dubenco CPetrașcu VDascălu G(2024)Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian ReviewsMathematics10.3390/math1203045612:3(456)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030456
Alsmadi MAlzaqebah MJawarneh SALmarashdeh IAl-Betar MAlwohaibi MAl-Mulla NAhmed EAL Smadi A(2024)Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clusteringJournal of Big Data10.1186/s40537-024-00930-911:1Online publication date: 9-May-2024
https://doi.org/10.1186/s40537-024-00930-9
Bhattacharya AChakrabarti TBasu SKnott APedreschi DChatila RLeavy SEyers DTeal PBiecek P(2024)Towards a crowdsourced framework for online hate speech moderation - a case study in the Indian political scenarioCompanion Publication of the 16th ACM Web Science Conference10.1145/3630744.3663607(75-84)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3663607
Shokouhyar SMaghsoudi MKhanizadeh SJorfi S(2024)Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based studyAnnals of Operations Research10.1007/s10479-024-06119-w341:1(313-348)Online publication date: 26-Jun-2024
https://doi.org/10.1007/s10479-024-06119-w
Kamble VHaribhakta Y(2024)Bridging the Gap: Condensing Knowledge Graphs for Metaphor Processing by Visualizing Relationships in Figurative and Literal ExpressionsMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-3-031-62217-5_28(339-351)Online publication date: 11-Jun-2024
https://doi.org/10.1007/978-3-031-62217-5_28
Shrivastav SBag S(2024)Blockchain technology in circular economy: Unpacking the potential issues and critical echoes through data triangulation and natural language processingBusiness Strategy and the Environment10.1002/bse.3763Online publication date: 8-Apr-2024
https://doi.org/10.1002/bse.3763
Hettiarachchi HAdedoyin-Olowe MBhogal JGaber M(2023)WhatsUp: An event resolution approach for co-occurring events in social mediaInformation Sciences10.1016/j.ins.2023.01.001625(553-577)Online publication date: May-2023
https://doi.org/10.1016/j.ins.2023.01.001
Artese MGagliardi I(2022)Methods, Models and Tools for Improving the Quality of Textual AnnotationsModelling10.3390/modelling30200153:2(224-242)Online publication date: 12-Apr-2022
https://doi.org/10.3390/modelling3020015
Alkomah FMa X(2022)A Literature Review of Textual Hate Speech Detection Methods and DatasetsInformation10.3390/info1306027313:6(273)Online publication date: 26-May-2022
https://doi.org/10.3390/info13060273
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.

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