The concept of natural language processing has become one of the preferred methods to better understand consumers, especially in recent years when digital technologies and research methods have developed exponentially. It has become apparent that when responding to international consumers through multiple platforms and speaking in the same language in which the consumers express themselves, companies are improving their standings within the public sphere. Natural Language Processing for Global and Local Business provides research exploring the theoretical and practical phenomenon of natural language processing through different languages and platforms in terms of today's conditions. Featuring coverage on a broad range of topics such as computational linguistics, information engineering, and translation technology, this book is ideally designed for IT specialists, academics, researchers, students, and business professionals seeking current research on improving and understanding the consumer experience.

"This book explores the theoretical and practical phenomenon of natural language processing through different languages and platforms in terms of today's conditions"--

The rise of internet and social media usage in the past couple of decades has presented a very useful tool for many different industries and fields to utilize. With much of the world’s population writing their opinions on various products and services in public online forums, industries can collect this data through various computational tools and methods. These tools and methods, however, are still being perfected in both collection and implementation. Sentiment analysis can be used for many different industries and for many different purposes, which could better business performance and even society. The Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines discusses the tools, methodologies, applications, and implementation of sentiment analysis across various disciplines and industries such as the pharmaceutical industry, government, and the tourism industry. It further presents emerging technologies and developments within the field of sentiment analysis and opinion mining. Covering topics such as electronic word of mouth (eWOM), public security, and user similarity, this major reference work is a comprehensive resource for computer scientists, IT professionals, AI scientists, business leaders and managers, marketers, advertising agencies, public administrators, government officials, university administrators, libraries, students and faculty of higher education, researchers, and academicians.

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

The advent of the internet and social media were landmarks in furthering communication technologies. Through social media websites, families, friends, and communities could connect in a way never seen. Though these websites are helpful tools in facilitating positive interaction, they have also allowed users to verbally attack and bully each other with no fear of repercussion. Moreover, online predators will often use these tools to harass, stalk, and in some cases even lure their victims. Particularly rampant among adolescents, these harmful actions must be mitigated in order to safeguard the mental health and physical safety of users. The Research Anthology on Combating Cyber-Aggression and Online Negativity discusses the research behind cyber-aggression and cyber bullying, as well as methods to predict and prevent online negativity. It presents policy, technological, and human intervention practices against cyber-aggression. Covering topics such as media literacy, demographic variables, and workplace cyberbullying, this major reference work is a critical resource for students and educators of higher education, libraries, social media administrators, government organizations, K-12 teachers, computer scientists, sociologists, psychologists, human resource managers, researchers, and academicians.

Many industries have struggled to realize the importance of modern technology, but none more so than the construction industry. By employing various computer-aided management systems, construction businesses have increased their profitability and the systematic way their companies function. Managing Business in the Civil Construction Sector Through Information Communication Technologies supplies a compendium of innovative research that highlights the use of computer-aided design and tools and the vital role that such forms of information technology have to play in the actual production activities of any civil construction company. Subsequent chapters focus on equally vital areas such as that of construction management, contracts management, materials management, human resource management, and enterprise resource planning. Chapters on cloud computing technology, internet of things, and artificial intelligence enable readers to acquire an overview and grasp the basics of these latest trending technologies. This book is ideally designed for construction firms, students, entrepreneurs, industry professionals, IT consultants, and academicians.

As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.

Environmental, social, and corporate governance (ESG) risk considers the nonfinancial risks that could arise in a business, such as sustainability, brand reputation, legal aspects, ethics, and more. As businesses all have their own risk profiles, there is a need for risk management and mitigation that is unique for each company. Because of this variability, the study on ESG risk factors and motives of incorporating the ESG perspective into business models are crucial yet challenging. Therefore, it is important to understand how companies are adapting and mitigating ESG risk in diverse types of businesses. Adapting and Mitigating Environmental, Social, and Governance Risk in Business examines processes in enterprises that can increase the sustainability of business models and their coherence with the assumptions of the concept of sustainable development and ESG risk. Furthermore, the book explores how enterprises operating in different sectors are adapting their business models towards sustainability in order to create sustainable value. This book is a valuable tool for managers, executives, entrepreneurs, practitioners, academicians, researchers, and graduate students in finance, business, and management.

Due to such factors as poor economic conditions, climate change, and conflict, food security remains an issue around the world and especially in developing nations. Rapid changes in technology over the last decade has brought a renewed focus on how information and communication technologies (ICTs) and application systems are deployed to improve rural competitiveness. Unfortunately, agricultural stakeholders in developing countries, particularly in Africa, have not been able to reap comparable benefits from adopting agricultural information systems as compared to their counterparts in the developed economies. Understanding the challenges that hinder the effective adoption of agricultural information systems and identifying opportunities or innovations is imperative to improve the agricultural sectors and overcome the problems in these developing economies. Opportunities and Strategic Use of Agribusiness Information Systems is an essential reference book that examines the key challenges that hinder the effective adoption of agricultural information systems. Moreover, it identifies and evaluates opportunities for the strategic deployment of ICTs and information systems to drive agricultural development for the benefit of agricultural sector stakeholders in emerging countries. While highlighting such topics as agricultural entrepreneurship, food value chain, and innovation systems, it is intended to provide sound and relevant frameworks and tools that will aid agricultural industry practitioners, smallholder farmers, and managers of agricultural extension systems looking to make more effective and responsible decisions when selecting, planning, deploying, and managing agribusiness information systems. It is additionally targeted for agricultural funding organizations, government policymakers, academicians, researchers, and students concerned with exploiting the potential of a variety of ICTs and information systems in the quest to achieve food security and poverty reduction in emerging economies.