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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.
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.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography
Innovation Strategies in Environmental Science introduces and examines economically viable innovations to optimize performance and sustainability. By exploring short and long-term strategies for the development of networks and platform development, along with suggestions for open innovation, chapters discuss sustainable development ideas in key areas such as urban management/eco-design and conclude with case studies of end-user-inclusive strategies for the water supply sector. This book is an important resource for environmental and sustainability scientists interested in introducing innovative practices into their work to minimize environmental impacts. - Presents problem-oriented research and solutions - Offers strategies for minimizing or avoiding the environmental impacts of industrial production - Includes case studies on topics such as end user-inclusive innovation strategies for the water supply sector
The object of this book is to highlight how the nascent field of sustainability science is addressing a key challenges for scientists; that is, understanding the workings of complex systems especially when humans are involved. A consistent thread in the sustainability science movement is the wide acknowledgement that greater degrees of integration across what are now segmented dimensions of extant Science and Technology systems will be a key factor in matching the most appropriate science and technology solutions to specific sustainability problems in specific places.
Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.
Grounded in solid research, Social Media in the Public Sector explores the myriad uses of social media in the public sector and combines existing practices with theories of public administration, networked governance, and information management. Comprehensive in scope, the book includes best practices, the strategic, managerial, administrative, and procedural aspects of using social media, and explains the theoretical dimensions of how social behavior affects the adoption of social media technologies. Praise for Social Media in the Public Sector "Mergel has produced a foundational work that combines the best kind of scholarship with shoe-leather reporting and anthropology that highlights the debates that government agencies are struggling to resolve and the fruits of their efforts as they embrace the social media revolution. Social Media in the Public Sector is a first and sets a high standard against which subsequent analysis will be measured." —Lee Rainie, director, Pew Research Center's Internet & American Life Project "Mergel is an award-winning author who again wields her story skills in this book. She excels in explaining in concrete, practical terms how government managers can use social media to serve the public. Her book puts years of research into one handy guide. It's practical. It's readable. And it's an essential read." —John M. Kamensky, senior fellow, IBM Center for The Business of Government "Mergel moves beyond the hype with detailed, comprehensive research on social media technologies, use, management, and policies in government. This book should be required reading for researchers and public managers alike." —Jane Fountain, professor and director, National Center for Digital Government, University of Massachusetts Amherst "Comprehensive and compelling, Social Media in the Public Sector makes the case that to achieve Government 2.0, agencies must first adopt Web 2.0 social technologies. Mergel explains both how and why in this contemporary study of traditional institutions adopting and adapting to new technologies." —Beth Simone Noveck, United States Deputy Chief Technology Officer (2009-2011)
Feminist scholars and activists explore the relationships among humans, animals, and the natural environment.
The practice of social and ethical accounting is emerging as a key tool for companies in the 1990s in response to calls for greater transparency and accountability to different stakeholders, and as a means for managing companies in increasingly complex situations where social and environmental issues are significant in securing business success. This is the first book to address the practice of social and ethical accounting, auditing and reporting, and its implications for the development of corporate social, ethical and environmental responsibility. It includes ten case studies, as well as an historical overview of the development of social and ethical accounting and reporting. The editors introduce a methodological framework that allows emerging practice worldwide to be analysed, understood and improved; and the case studies are written by the practitioners, giving insight into the experiences described. This innovative book, written by internationally acknowledged leaders in the field, will be of enormous value to business managers, particularly those with responsibility for corporate affairs, human resources, environmental management, financial management, or planning. It will also be a useful text for business students.