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Text mining and sentiment analysis in climate change and environmental sustainability

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text mining and sentiment analysis in climate change and environmental sustainability/ Rohit Bansal, Fazla Rabby, Ridhima Sharma, Dalima Parwani, Arti Gupta, editors.
other author:	Gupta, Arti.
Published:	Hershey, Pennsylvania :IGI Global, : 2025.,
Description:	1 online resource (510 p.)
[NT 15003449]:	Preface -- Chapter 1. A Hybrid ML Sentiment Analysis for Climate Change Management in Social Media -- Chapter 2. Emphasizing the Environmental Issue by Machine Learning-Based Sentiment Analysis -- Chapter 3. Depicting SustainabilityAwareness in Turkey-Syria Earthquake News Coverage via Sentiment Analysis -- Chapter 4. Mining Minds Sentiment Analysis of Public Perception and Awareness on Climate Change for Environmental Sustainability -- Chapter 5. Analysis of Corporation Trends in Environmental Protection Using VOS Viewer -- Chapter 6. Effect on Manufacturing Industries Benefit From Life Cycle Sustainability Assessment of Environmental and Social Criteria -- Chapter 7. Development of Sustainable Goals Emissions Reduction Value to Measure Climate Change -- Chapter 8. Harnessing AI for Climate Resilience: Opportunities and Challenges in Combating Climate Change -- Chapter 9. Monitoring of Environmental Analysis in Twitter Dealt With Pollution Probability -- Chapter 10. Analysing Public Sentiment Towards Climate Change Using Natural Language Processing -- Chapter 11. Combining Clinical Data With Neuro Images to Identify the Treatment Resistant in Depression by NLP -- Chapter 12. NLP Techniques to Achieve the Detection of Climate Change in Text Corpora -- Chapter 13. Public Engagement and Participation in Climate Change and Environmental Sustainability: Enabling Adaptability, Designing for Inclusion, and Embracing Complexity -- Chapter 14. Analysing the Textual Analysis of Different NLP Techniques to Classify the Stability of Corporate Reporting -- Chapter 15. Sustainable Climate Change Analysis of Renewable Power -- Chapter 16. Analysis of Contemporary Trends in Industrial Stability Across Various Countries Through Text Mining -- Chapter 17. Navigating the Abyss: Overcoming Challenges in Text Mining for Climate Science -- Chapter 18. Drivers and Impacts of Text Mining on Climate Change -- Chapter 19. Integration of Big Data Text Mining and Sentiment Analysis on Public Prediction of Business Environment -- Chapter 20. Sentiment Analysis and Text Mining in Environmental Sustainability and Climate Change -- Chapter 21. Text Mining in Climate Change Communication and Corporate Sustainability Reporting -- Compilation of References -- About the Contributors -- Index.
Subject:	Climatic changes - Technological innovations. -
Online resource:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7230-2
ISBN:	9798369372326

Text mining and sentiment analysis in climate change and environmental sustainability
Text mining and sentiment analysis in climate change and environmental sustainability[electronic resource] /Rohit Bansal, Fazla Rabby, Ridhima Sharma, Dalima Parwani, Arti Gupta, editors. - Hershey, Pennsylvania :IGI Global,2025. - 1 online resource (510 p.)

Includes bibliographical references and index.

Preface -- Chapter 1. A Hybrid ML Sentiment Analysis for Climate Change Management in Social Media -- Chapter 2. Emphasizing the Environmental Issue by Machine Learning-Based Sentiment Analysis -- Chapter 3. Depicting SustainabilityAwareness in Turkey-Syria Earthquake News Coverage via Sentiment Analysis -- Chapter 4. Mining Minds Sentiment Analysis of Public Perception and Awareness on Climate Change for Environmental Sustainability -- Chapter 5. Analysis of Corporation Trends in Environmental Protection Using VOS Viewer -- Chapter 6. Effect on Manufacturing Industries Benefit From Life Cycle Sustainability Assessment of Environmental and Social Criteria -- Chapter 7. Development of Sustainable Goals Emissions Reduction Value to Measure Climate Change -- Chapter 8. Harnessing AI for Climate Resilience: Opportunities and Challenges in Combating Climate Change -- Chapter 9. Monitoring of Environmental Analysis in Twitter Dealt With Pollution Probability -- Chapter 10. Analysing Public Sentiment Towards Climate Change Using Natural Language Processing -- Chapter 11. Combining Clinical Data With Neuro Images to Identify the Treatment Resistant in Depression by NLP -- Chapter 12. NLP Techniques to Achieve the Detection of Climate Change in Text Corpora -- Chapter 13. Public Engagement and Participation in Climate Change and Environmental Sustainability: Enabling Adaptability, Designing for Inclusion, and Embracing Complexity -- Chapter 14. Analysing the Textual Analysis of Different NLP Techniques to Classify the Stability of Corporate Reporting -- Chapter 15. Sustainable Climate Change Analysis of Renewable Power -- Chapter 16. Analysis of Contemporary Trends in Industrial Stability Across Various Countries Through Text Mining -- Chapter 17. Navigating the Abyss: Overcoming Challenges in Text Mining for Climate Science -- Chapter 18. Drivers and Impacts of Text Mining on Climate Change -- Chapter 19. Integration of Big Data Text Mining and Sentiment Analysis on Public Prediction of Business Environment -- Chapter 20. Sentiment Analysis and Text Mining in Environmental Sustainability and Climate Change -- Chapter 21. Text Mining in Climate Change Communication and Corporate Sustainability Reporting -- Compilation of References -- About the Contributors -- Index.

"With the rising need to address shifting global temperatures, precipitation patterns, and atmospheric conditions, text mining and sentiment analysis play a crucial role in managing climate change and promoting environmental sustainability. These techniques provide valuable insights to support decision-making, stakeholder engagement, risk management, policymaking, and corporate communication efforts to address the changing climate and respond to important crises. Further research into text mining and sentiment analysis is necessary to understand the public's perception on climate change, address corporate concerns, and identify emerging risks associated with the environment. Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability provides updated information on the emergence and role of text mining and sentiment analysis in predicting climate change and promoting environmental sustainability. It covers emerging trends involved in the nexus of text mining, sentiment analysis, climate change and environmental sustainability. This book covers topics such as environmental science, sustainable development, and machine learning, and is a useful resource for climatologists, environmental scientists, computer engineers, data scientists, academicians, and researchers."--

Mode of access: World Wide Web.

ISBN: 9798369372326Subjects--Topical Terms:

3786839
Climatic changes
--Technological innovations.Subjects--Index Terms:

Artificial Intelligence.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QC903 / .T49 2025eb

Dewey Class. No.: 363.73874

Text mining and sentiment analysis in climate change and environmental sustainability
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245 0 0 $a Text mining and sentiment analysis in climate change and environmental sustainability $h [electronic resource] / $c Rohit Bansal, Fazla Rabby, Ridhima Sharma, Dalima Parwani, Arti Gupta, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2025.
300 $a 1 online resource (510 p.)
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Chapter 1. A Hybrid ML Sentiment Analysis for Climate Change Management in Social Media -- Chapter 2. Emphasizing the Environmental Issue by Machine Learning-Based Sentiment Analysis -- Chapter 3. Depicting SustainabilityAwareness in Turkey-Syria Earthquake News Coverage via Sentiment Analysis -- Chapter 4. Mining Minds Sentiment Analysis of Public Perception and Awareness on Climate Change for Environmental Sustainability -- Chapter 5. Analysis of Corporation Trends in Environmental Protection Using VOS Viewer -- Chapter 6. Effect on Manufacturing Industries Benefit From Life Cycle Sustainability Assessment of Environmental and Social Criteria -- Chapter 7. Development of Sustainable Goals Emissions Reduction Value to Measure Climate Change -- Chapter 8. Harnessing AI for Climate Resilience: Opportunities and Challenges in Combating Climate Change -- Chapter 9. Monitoring of Environmental Analysis in Twitter Dealt With Pollution Probability -- Chapter 10. Analysing Public Sentiment Towards Climate Change Using Natural Language Processing -- Chapter 11. Combining Clinical Data With Neuro Images to Identify the Treatment Resistant in Depression by NLP -- Chapter 12. NLP Techniques to Achieve the Detection of Climate Change in Text Corpora -- Chapter 13. Public Engagement and Participation in Climate Change and Environmental Sustainability: Enabling Adaptability, Designing for Inclusion, and Embracing Complexity -- Chapter 14. Analysing the Textual Analysis of Different NLP Techniques to Classify the Stability of Corporate Reporting -- Chapter 15. Sustainable Climate Change Analysis of Renewable Power -- Chapter 16. Analysis of Contemporary Trends in Industrial Stability Across Various Countries Through Text Mining -- Chapter 17. Navigating the Abyss: Overcoming Challenges in Text Mining for Climate Science -- Chapter 18. Drivers and Impacts of Text Mining on Climate Change -- Chapter 19. Integration of Big Data Text Mining and Sentiment Analysis on Public Prediction of Business Environment -- Chapter 20. Sentiment Analysis and Text Mining in Environmental Sustainability and Climate Change -- Chapter 21. Text Mining in Climate Change Communication and Corporate Sustainability Reporting -- Compilation of References -- About the Contributors -- Index.
520 3 $a "With the rising need to address shifting global temperatures, precipitation patterns, and atmospheric conditions, text mining and sentiment analysis play a crucial role in managing climate change and promoting environmental sustainability. These techniques provide valuable insights to support decision-making, stakeholder engagement, risk management, policymaking, and corporate communication efforts to address the changing climate and respond to important crises. Further research into text mining and sentiment analysis is necessary to understand the public's perception on climate change, address corporate concerns, and identify emerging risks associated with the environment. Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability provides updated information on the emergence and role of text mining and sentiment analysis in predicting climate change and promoting environmental sustainability. It covers emerging trends involved in the nexus of text mining, sentiment analysis, climate change and environmental sustainability. This book covers topics such as environmental science, sustainable development, and machine learning, and is a useful resource for climatologists, environmental scientists, computer engineers, data scientists, academicians, and researchers."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
650 0 $a Climatic changes $x Technological innovations. $3 3786839
650 0 $a Sustainability $x Technological innovations $3 3793526
650 0 $a Text data mining. $3 3495453
650 0 $a Artificial intelligence $x Environmental aspects. $3 3598484
653 $a Artificial Intelligence.
653 $a Business Manufacturing.
653 $a Climate Change.
653 $a Emissions Reduction.
653 $a Environmental Science.
653 $a Machine Learning.
653 $a Natural Language Processing.
653 $a Pollution.
653 $a Sentiment Analysis.
653 $a Social Media.
653 $a Sociology.
653 $a Sustainable Development.
653 $a Text Mining.
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Gupta, Arti. $3 3386596
700 1 $a Parwani, Dalima. $3 3793524
700 1 $a Sharma, Ridhima. $3 3793525
700 1 $a Rabby, Fazla. $3 3793425
700 1 $a Bansal, Rohit, $d 1982- $3 3616726
710 2 $a IGI Global. $3 1361470
776 0 8 $i Print version: $z 9798369372302
856 4 0 $u https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7230-2