research-article

Public opinion matters: mining social media text for environmental management

Authors:

Matthew Kowalski,

Aparna S. Varde,

Gerard de Melo,

Robert W. TaylorAuthors Info & Claims

ACM SIGWEB Newsletter, Volume 2019, Issue Autumn

Article No.: 5, Pages 1 - 15

https://doi.org/10.1145/3352683.3352688

Published: 13 February 2020 Publication History

Abstract

Social media mining has proven useful in multiple research fields as a tool for public opinion extraction and analysis. Such mining can discover knowledge from unstructured data in booming social media sources that provide instant public responses and also capture long-term data. Environmental scientists have realized its potential and conducted various studies where public opinion matters. We focus our discussion in this article on mining social media text on environmental issues, with particular emphasis on sentiment analysis, fitting the theme of Data Science and Sustainability. The data science community today is interested in topics that overlap with environmental issues and their broader impacts on sustainability. Such work appeals to scientists focusing on areas such as smart cities, climate change and geo-informatics. Future issues emerging from this research include domain-specific multilingual mining, and advanced geo-location tagging with demographically focused sentiment analysis.

References

[1]

Baccianella, S., Esuli, A., and Sebastiani, F. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Intl. Conf. on Language Resources and Evaluation, LREC. Valletta, Malta.

[2]

Dahal, B., Kumar, S., and Li, Z. 2019. Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining, Springer 9, 24.

[3]

de Melo, G. 2017. Inducing conceptual embedding spaces from Wikipedia. In Proceedings of WWW 2017. ACM.

Digital Library

[4]

Dong, X. and de Melo, G. 2019. A robust self-learning framework for cross-lingual text classification. In Proceedings of EMNLP-IJCNLP 2019. ACL.

[5]

Du, X., Emebo, O., Varde, A., Tandon, N., Nag Chowdhury, S., and Weikum, G. 2016. Air quality assessment from social media and structured data. In IEEE Intl. Conf. on Data Engineering - Workshop on Health Data Management and Mining (ICDE-HDMM). Helsinki, Finland, 54--59.

[6]

Fellbaum, C.,Ed. 1998. WordNet: An Electronic Lexical Database. The MIT Press.

[7]

Gandhe, K., Varde, A., and Du, X. 2018. Sentiment analysis of Twitter data with hybrid learning for recommender applications. In IEEE Ubiquitous Computing, Electronics and Mobile Communications Conference (UEMCON). New York, NY, 57--63.

[8]

Gu, Y., Qian, Z., and Chen, F. 2016. From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies 67, 321--342.

[9]

Hollander, J. and Renski, H. 2015. Measuring urban attitudes using Twitter: An exploratory study. Working Paper WP15JH1, Lincoln Institute of Land Policy, USA.

[10]

Huang, Q., Cervone, G., and Zhang, G. 2017. A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data. Computers, Environment and Urban Systems 66, 23--37.

[11]

Nuortimo, K. 2018. Measuring public acceptance with opinion mining: The case of the energy industry with long-term coal R&D investment projects. Journal of Intelligence Studies in Business 8, 2, 6--22.

[12]

Nuortimo, K. and Harkonen, J. 2018. Opinion mining approach to study media-image of energy production - implications to public acceptance and market deployment. Renewable and Sustainable Energy Reviews 96, 210--217.

[13]

Pampoore-Thampi, A., Varde, A., and Yu, D. 2014. Mining GIS data to predict urban sprawl. In ACM Conference on Knowledge Discovery and Data Mining (KDD), Bloomberg Track. NYC, New York, 118--125.

[14]

Puri, M., Du, X., Varde, A., and de Melo, G. 2018. Mapping ordinances and tweets using smart city characteristics to aid opinion mining. In The Web Conference (WWW) Companion Volume. Lyon, France, 1721--1728.

Digital Library

[15]

Rozeva, A. and Zerkova, S. 2017. Assessing semantic similarity of texts - methods and algorithms. In Intl. Conf. on Applications of Mathematics in Engineering and Economics (AIP Conf. Proc). 1--8.

[16]

Sachdeva, S., McCaffrey, S., and Locke, D. 2016. Social media approaches to modeling wildfire smoke dispersion: Spatiotemporal and social scientific investigations. Information, Communication and Society 20, 8, 1146--1161.

[17]

Tandon, N., de Melo, G., Suchanek, F., and Weikum, G. 2014. WebChild: Harvesting and organizing commonsense knowledge from the web. In ACM International Conference on Web Search and Data Mining (WSDM). NYC, New York, 523--532.

[18]

Tandon, N., Varde, A., and de Melo, G. 2017. Commonsense knowledge in machine intelligence. ACM SIGMOD Record 46, 49--52.

Digital Library

[19]

Taylor, R. 2012. Urbanization, local government and planning for sustainability. Sustainability Science: the Emerging Paradigm and the Urban Environment.

[20]

TU-WIEN. 2015. European smart cities, technical report. Vienna University of Technology, Vienna Austria.

[21]

Wang, R., Mao, H., Wang, Y., Rae, C., and Shaw, W. 2018. Hyper-resolution monitoring of urban flooding with social media and crowdsourcing. Data, Computers and Geosciences 111, 139--147.

Digital Library

[22]

Wang, S., Paul, M., and Dredze, M. 2015. Social media as a sensor of air quality and public response in China. Journal of Medical Internet Research 17, 3.

[23]

Wang, S., Zhang, X., Cao, J., He, L., Stenneth, L., Yu, P., Li, Z., and Huang, Z. 2017. Computing urban traffic congestions by incorporating sparse GPS probe data and social media data. ACM Transactions on Information Systems 35, 4, 1--30.

Digital Library

[24]

Wu, Y., Xie, L., Huang, S., Li, P., Yuan, Z., and Liu, W. 2018. Using social media to strengthen public awareness of wildlife conservation. Ocean and Coastal Management 153, 76--83.

[25]

Zhan, X., Ukkusuri, S., and Zhu, F. 2014. Inferring urban land use using large-scale social media check-in data. Networks and Spatial Economics 14, 3, 647--667.

[26]

Zou, L., Lam, N., Cai, H., and Qiang, Y. 2018. Mining Twitter data for improved understanding of disaster resilience. Annals of the American Association of Geographers 108, 5, 1422--1441.

Cited By

Sun HLiu ZLian WWang G(2024)Social Network Public Opinion Analysis Using BERT-BMA in Big Data EnvironmentInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.35250917:1(1-18)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJITSA.352509
Lu HRui XLi RZhang GZhang ZWu M(2024)A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network AnalysisISPRS International Journal of Geo-Information10.3390/ijgi1310035213:10(352)Online publication date: 4-Oct-2024
https://doi.org/10.3390/ijgi13100352
Rusli NYusran GNordin NRosley M(2024)Investigating the impact of urban green space quality on subjective well-being via social media analyticsJournal of Urban Design10.1080/13574809.2024.2364275(1-20)Online publication date: 26-Jun-2024
https://doi.org/10.1080/13574809.2024.2364275
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Information & Contributors

Information

Published In

cover image ACM SIGWEB Newsletter

ACM SIGWEB Newsletter Volume 2019, Issue Autumn

Autumn 2019

39 pages

ISSN:1931-1745

EISSN:1931-1435

DOI:10.1145/3352683

Editor:
Jessica Rubart

Issue’s Table of Contents

Copyright © 2020 Copyright is held by the owner/author(s).

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2020

Published in SIGWEB Volume 2019, Issue Autumn

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Cited By

Sun HLiu ZLian WWang G(2024)Social Network Public Opinion Analysis Using BERT-BMA in Big Data EnvironmentInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.35250917:1(1-18)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/IJITSA.352509
Lu HRui XLi RZhang GZhang ZWu M(2024)A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network AnalysisISPRS International Journal of Geo-Information10.3390/ijgi1310035213:10(352)Online publication date: 4-Oct-2024
https://doi.org/10.3390/ijgi13100352
Rusli NYusran GNordin NRosley M(2024)Investigating the impact of urban green space quality on subjective well-being via social media analyticsJournal of Urban Design10.1080/13574809.2024.2364275(1-20)Online publication date: 26-Jun-2024
https://doi.org/10.1080/13574809.2024.2364275
Hidalgo RSalah NChandra Jetty RJetty AVarde A(2024)Personalizing Text-to-Image Diffusion Models by Fine-Tuning Classification for AI ApplicationsIntelligent Systems and Applications10.1007/978-3-031-47721-8_44(642-658)Online publication date: 10-Jan-2024
https://doi.org/10.1007/978-3-031-47721-8_44
Vashi HYadav JVarde A(2024)Predictive Analysis of Bank Marketing for Financial Decision Support and Smart EconomyIntelligent Systems and Applications10.1007/978-3-031-47715-7_33(474-490)Online publication date: 30-Jan-2024
https://doi.org/10.1007/978-3-031-47715-7_33
Ibrohim MBosco CBasile V(2023)Sentiment Analysis for the Natural Environment: A Systematic ReviewACM Computing Surveys10.1145/360460556:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3604605
Varde ADe Melo GDong B(2023)Temporal Ordinance Mining for Event-Driven Social Media Reaction AnalyticsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587674(1225-1227)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587674
Sofian NKeikhosrokiani PAsl M(2022)Opinion Mining and Text Analytics of Reader Reviews of Yoko Ogawa's The Housekeeper and the Professor in GoodreadsHandbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media10.4018/978-1-7998-9594-7.ch010(240-262)Online publication date: 18-Feb-2022
https://doi.org/10.4018/978-1-7998-9594-7.ch010
Bai H(2022)Social Public Opinion Communication and Network Legal Management Based on Artificial Intelligence Cognitive Wireless NetworkMobile Information Systems10.1155/2022/11279312022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1127931
Puri MVarde Ade Melo G(2022)Commonsense based text mining on urban policyLanguage Resources and Evaluation10.1007/s10579-022-09584-657:2(733-763)Online publication date: 25-Feb-2022
https://doi.org/10.1007/s10579-022-09584-6
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