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| eda for sentiment analysis: Exploratory Data Analysis with Python Cookbook Ayodele Oluleye, 2023-06-30 Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience. |
| eda for sentiment analysis: Multifaceted approaches for Data Acquisition, Processing & Communication Chinmay Chakraborty, Manisha Guduri, B Sandhya, K Shyamala, 2024-06-24 The objective of the conference is to bring to focus the recent technological advancements across all the stages of data analysis including acquisition, processing, and communication. Advancements in acquisition sensors along with improved storage and computational capabilities, have stimulated the progress in theoretical studies and state-of-the-art real-time applications involving large volumes of data. This compels researchers to investigate the new challenges encountered, where traditional approaches are incapable of dealing with large, complicated new forms of data. |
| eda for sentiment analysis: Applied Machine Learning Explainability Techniques Aditya Bhattacharya, 2022-07-29 Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various explainability methods for designing robust and scalable explainable ML systems • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems • Design user-centric explainable ML systems using guidelines provided for industrial applications Book Description Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn • Explore various explanation methods and their evaluation criteria • Learn model explanation methods for structured and unstructured data • Apply data-centric XAI for practical problem-solving • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others • Discover industrial best practices for explainable ML systems • Use user-centric XAI to bring AI closer to non-technical end users • Address open challenges in XAI using the recommended guidelines Who this book is for This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher. |
| eda for sentiment analysis: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media Gupta, Brij B., Perakovi?, Dragan, Abd El-Latif, Ahmed A., Gupta, Deepak, 2021-12-31 Social media sites are constantly evolving with huge amounts of scattered data or big data, which makes it difficult for researchers to trace the information flow. It is a daunting task to extract a useful piece of information from the vast unstructured big data; the disorganized structure of social media contains data in various forms such as text and videos as well as huge real-time data on which traditional analytical methods like statistical approaches fail miserably. Due to this, there is a need for efficient data mining techniques that can overcome the shortcomings of the traditional approaches. Data Mining Approaches for Big Data and Sentiment Analysis in Social Media encourages researchers to explore the key concepts of data mining, such as how they can be utilized on online social media platforms, and provides advances on data mining for big data and sentiment analysis in online social media, as well as future research directions. Covering a range of concepts from machine learning methods to data mining for big data analytics, this book is ideal for graduate students, academicians, faculty members, scientists, researchers, data analysts, social media analysts, managers, and software developers who are seeking to learn and carry out research in the area of data mining for big data and sentiment. |
| eda for sentiment analysis: Automated Data Analytics Soraya Sedkaoui, 2024-10-11 The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models. Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons. This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress. |
| eda for sentiment analysis: Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering Bibudhendu Pati, Chhabi Rani Panigrahi, Prasant Mohapatra, Kuan-Ching Li, 2022-09-21 This book gathers high-quality research papers presented at the 6th International Conference on Advanced Computing and Intelligent Engineering (ICACIE 2021) organized by Bhubaneswar Institute of Technology, Bhubaneswar, Odisha, India, during December 23–24, 2021. It includes sections describing technical advances and the latest research in the fields of computing and intelligent engineering. Intended for graduate students and researchers working in the disciplines of computer science and engineering, the proceedings also appeal to researchers in the field of electronics, as they cover hardware technologies and future communication technologies. |
| eda for sentiment analysis: Creating and Sustaining an Information Governance Program Helge, Kris, Rookey, Caitlin A., 2024-04-26 We live in an era defined by data proliferation and digital transformation, and the effective management of information has become a concern for organizations across the globe. Creating and Sustaining an Information Governance Program is a comprehensive academic guide that delves into the intricate realm of Information Governance (IG), focusing on the key components and strategies essential for establishing and perpetuating a robust IG program. This book elucidates the intricacies of establishing and nurturing an information governance program, and it equips readers with the knowledge and tools to navigate the challenges and opportunities inherent in this endeavor. It delves into the cultural shifts, communication strategies, and training methods necessary for success. It emphasizes the vital importance of collaboration across organizational silos, the cultivation of administrative support, securing appropriate funding, and educating stakeholders on the purpose and benefits of an IG program. This book is ideal for individuals across academia, corporate sectors, government agencies, and for-profit and not-for-profit organizations. Its insights are universally applicable, spanning industries such as law firms, general corporate environments, government entities, educational institutions, and businesses of all sizes. Creating and Sustaining an Information Governance Program guides organizations of all stripes toward effective information governance, compliance, and risk mitigation in a data-centric world. |
| eda for sentiment analysis: Graphical Exploratory Data Analysis S. H. C. DuToit, A. G. W. Steyn, R. H. Stumpf, 2012-12-06 Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization has emerged from many years of experience in teaching students, in research, and especially from engaging in statistical consulting work in a variety of subject fields. Consequently, we were somewhat surprised to discover that a comprehen sive, yet simple presentation of graphical exploratory techniques for the data analyst was not available. Generally books on the subject were either too incomplete, stopping at a histogram or pie chart, or were too technical and specialized and not linked to readily available computer programs. Many of these graphical techniques have furthermore only recently appeared in statis tical journals and are thus not easily accessible to the statistically unsophis ticated data analyst. This book, therefore, attempts to give a sound overview of most of the well-known and widely used methods of analyzing and portraying data graph ically. Throughout the book the emphasis is on exploratory techniques. Real izing the futility of presenting these methods without the necessary computer programs to actually perform them, we endeavored to provide working com puter programs in almost every case. Graphic representations are illustrated throughout by making use of real-life data. Two such data sets are frequently used throughout the text. In realizing the aims set out above we avoided intricate theoretical derivations and explanations but we nevertheless are convinced that this book will be of inestimable value even to a trained statistician. |
| eda for sentiment analysis: Text Mining with R Julia Silge, David Robinson, 2017-06-12 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. |
| eda for sentiment analysis: Introduction to Text Analytics Emily Ohman, 2024-11-01 This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data. It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach. Written specifically for students and early career researchers this pragmatic manual will: • Contextualise your learning with real-world data and engaging case studies. • Encourage the application of your new skills with reflective questions. • Enhance your ability to be critical, and reflective, when dealing with imperfect data. Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. |
| eda for sentiment analysis: HCI International 2023 – Late Breaking Papers Hirohiko Mori, Yumi Asahi, Adela Coman, Simona Vasilache, Matthias Rauterberg, 2023-11-20 This seven-volume set LNCS 14054-14060 constitutes the proceedings of the 25th International Conference, HCI International 2023, in Copenhagen, Denmark, in July 2023. For the HCCII 2023 proceedings, a total of 1578 papers and 396 posters was carefully reviewed and selected from 7472 submissions. Additionally, 267 papers and 133 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work”. These papers were organized in the following topical sections: HCI Design and User Experience; Cognitive Engineering and Augmented Cognition; Cultural Issues in Design; Technologies for the Aging Population; Accessibility and Design for All; Designing for Health and Wellbeing; Information Design, Visualization, Decision-making and Collaboration; Social Media, Creative Industries and Cultural Digital Experiences; Digital Human Modeling, Ergonomics and Safety; HCI in Automated Vehicles and Intelligent Transportation; Sustainable Green Smart Cities and Smart Industry; eXtended Reality Interactions; Gaming and Gamification Experiences; Interacting with Artificial Intelligence; Security, Privacy, Trust and Ethics; Learning Technologies and Learning Experiences; eCommerce, Digital Marketing and eFinance. |
| eda for sentiment analysis: Computational Intelligence in Data Science Lekshmi Kalinathan, Priyadharsini R., Madheswari Kanmani, Manisha S., 2022-09-28 This book constitutes the refereed post-conference proceedings of the Fifth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2022, held virtually, in March 2022. The 28 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science. |
| eda for sentiment analysis: Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics Triwiyanto Triwiyanto, |
| eda for sentiment analysis: Data Science and Data Analytics Dinesh Kumar Arivalagan, 2024-07-31 Data Science and Data Analytics explores the foundational concepts, methodologies, and tools that drive data-driven decision-making in various industries. This book provides a comprehensive overview of data collection, processing, analysis, and visualization techniques, emphasizing practical applications and real-world case studies. Readers will gain insights into statistical methods, machine learning algorithms, and the importance of data ethics, equipping them with the knowledge to harness the power of data for informed decision-making and strategic planning in an increasingly data-centric world. |
| eda for sentiment analysis: Modern Artificial Intelligence and Data Science 2024 Abdellah Idrissi, |
| eda for sentiment analysis: Quantitative Investment Analysis CFA Institute, 2020-09-07 Whether you are a novice investor or an experienced practitioner, Quantitative Investment Analysis, 4th Edition has something for you. Part of the CFA Institute Investment Series, this authoritative guide is relevant the world over and will facilitate your mastery of quantitative methods and their application in todays investment process. This updated edition provides all the statistical tools and latest information you need to be a confident and knowledgeable investor. This edition expands coverage of Machine Learning algorithms and the role of Big Data in an investment context along with capstone chapters in applying these techniques to factor modeling, risk management and backtesting and simulation in investment strategies. The authors go to great lengths to ensure an even treatment of subject matter, consistency of mathematical notation, and continuity of topic coverage that is critical to the learning process. Well suited for motivated individuals who learn on their own, as well as a general reference, this complete resource delivers clear, example-driven coverage of a wide range of quantitative methods. Inside you'll find: Learning outcome statements (LOS) specifying the objective of each chapter A diverse variety of investment-oriented examples both aligned with the LOS and reflecting the realities of todays investment world A wealth of practice problems, charts, tables, and graphs to clarify and reinforce the concepts and tools of quantitative investment management You can choose to sharpen your skills by furthering your hands-on experience in the Quantitative Investment Analysis Workbook, 4th Edition (sold separately)—an essential guide containing learning outcomes and summary overview sections, along with challenging problems and solutions. |
| eda for sentiment analysis: Fun with Data Analysis and BI Nitin Sethi, 2024-08-29 DESCRIPTION Fun with Data Analysis and BI teaches you how to turn raw data into actionable insights using business intelligence tools. It equips you with essential skills to make data-driven decisions and effectively communicate findings. This book is designed to guide you through learning SQL from the ground up. Starting with installation and environment setup, it covers everything from building databases and creating tables to mastering SQL queries. Alongside theoretical concepts, you will engage in hands-on projects that demonstrate practical applications, including market analysis using Python to track stock trends and churn analysis to understand customer behavior. Each chapter concludes with MCQs to test your knowledge. The book also introduces you to Tableau, a powerful tool for creating visualizations without writing code, with step-by-step instructions on how to use it for your data projects. By the end of this book, you will be equipped with the skills to extract valuable insights from complex datasets, visualize data in compelling ways, and make data-driven decisions that positively impact your organization. KEY FEATURES ● In-depth coverage of SQL, Python, ML, and Tableau for all skill levels. ● Hands-on projects to transform raw information into valuable data insights. ● Practical examples and end-to-end solutions for mastering data science concepts. WHAT YOU WILL LEARN ● Install and set up SQL environments, create databases, develop tables, and write effective SQL queries. ● Use Python to analyze stock market data, create clusters, and support data-driven decisions. ● Apply ML to understand consumer behavior, predict churn, and improve retention. ● Design striking data visuals with Tableau, enhancing data presentation skills without coding. ● Gain hands-on experience by working on complete projects, from data preparation to final output. WHO THIS BOOK IS FOR Whether you are a business analyst, data scientist, or aspiring data professional, this book provides the essential knowledge and practical guidance to excel in the field of data analysis. TABLE OF CONTENTS 1. E-Ticket Booking 2. Creating Games on Python 3. Introduction to Sentiment Analysis 4. Sentiment Analysis on E-Commerce: Product Reviews 5. Sentiment Analysis on X 6. Stroke Prediction 7. Movie Review Sentiment Analysis 8. Stock Market Data Analysis 9. Customer Data Analysis 10. Analyzing Sports Data in Tableau 11. Office Supplies Dashboard Using Tableau 12. COVID Dashboard Using Tableau |
| eda for sentiment analysis: Industrial Engineering in the Industry 4.0 Era Numan M. Durakbasa, |
| eda for sentiment analysis: Agency and Institutions in Sport Mathew Dowling, Jonathan Robertson, Marvin Washington, Becca Leopkey, Dana Ellis, 2024-08-30 This book offers a unique insight into the role of individuals and organisations in shaping institutional arrangements within the context of sport. Institutional approaches can be used to examine the complex relationships between sport organisations and their broader environment and can help explain some of the most fundamental questions about the nature of how sport is organised including why are many sport organisations so similar? Why do they adopt practices that are seemingly irrational? And how can we explain how change occurs within sport organisations? In drawing upon contemporary scholarship and empirical evidence collected by internationally recognized experts within sport, this book provides a contemporary collection of studies that advances the understanding of agency in institutions through sport. In doing so, the chapters in this book bridge the theoretical divide between mainstream management and sport management to help facilitate a joint venture for future research. This book will be essential reading for advanced undergraduate or postgraduate students on sport or sport-related courses and researchers interested in institutional analysis and its potential application to sport. The chapters in this book were originally published as a special issue of European Sport Management Quarterly. |
| eda for sentiment analysis: Web Engineering Kostas Stefanidis, 2024 This book constitutes the proceedings of the 24th International Conference, ICWE 2024, held in Tampere, Finland, during June 17-20, 2024. The 16 full papers and 8 short papers included in this volume were carefully reviewed and selected from 66 submissions. This volume includes all the accepted papers across various conference tracks. The ICWE 2024 theme, Ethical and Human-Centric Web Engineering: Balancing Innovation and Responsibility, invited discussions on creating Web technologies that are not only innovative but also ethical, transparent, privacy-focused, trustworthy, and inclusive, putting human needs and well-being at the core. |
| eda for sentiment analysis: Proceedings of the 19th International Conference on Computing and Information Technology (IC2IT 2023) Phayung Meesad, Sunantha Sodsee, Watchareewan Jitsakul, Sakchai Tangwannawit, 2023-05-23 This book gathers the high-quality papers presented at the 19th International Conference on Computing and Information Technology (IC2IT2023), held on May 18–19, 2023, in Bangkok, Thailand. The book presents an original research work for both academic and industry domains, which is aiming to show valuable knowledge, skills and experiences in the field of computing and information technology. The topics covered in the book include natural language processing, image processing, intelligent systems and algorithms, as well as machine learning. These lead to the major research directions for innovating computational methods and applications of information technology. |
| eda for sentiment analysis: The Art of Data Science Roger D. Peng, Elizabeth Matsui, 2016-06-08 This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.--Leanpub.com. |
| eda for sentiment analysis: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results. |
| eda for sentiment analysis: The Deep Learning Architect's Handbook Ee Kin Chin, 2023-12-29 Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book. |
| eda for sentiment analysis: MULTIDISCIPLINARY APPROACHES FOR SUSTAINABLE DEVELOPMENT Anshuman Tripathi, Shilpi Birla, Mamta Soni, Jagrati Sahariya, Monica Sharma, 2024-11-25 In a world where the pace of technological advancement continues to accelerate, the imperative to ensure sustainable development has never been more pressing to address the same, the 1st International Conference on Multidisciplinary Approaches for Sustainable Development in Science & Technology (MASDST - 2024), took place at Manipal University Jaipur, Rajasthan, India, from 28th to 29th March 2024. Embracing the spirit of innovation and collaboration, this conference marks a significant milestone in the pursuit of sustainable solutions for our global challenges. |
| eda for sentiment analysis: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| eda for sentiment analysis: Data Labeling in Machine Learning with Python Vijaya Kumar Suda, 2024-01-31 Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started. |
| eda for sentiment analysis: Sustainable Farming through Machine Learning Suneeta Satpathy, Bijay Kumar Paikaray, Ming Yang, Arun Balakrishnan, 2024-11-25 This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices. Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies. This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications. |
| eda for sentiment analysis: Utilizing Emotional Experience for Best Learning Design Practices Sniderman, Sarah, 2024-10-22 Despite growing recognition of the impact of emotions on adult learning, academics and practitioners in our field still often overlook its critical role. Traditional approaches focus heavily on cognitive outcomes, neglecting the affective components of meaningful and relevant learning and development. This leaves learners ill-equipped to navigate the emotional challenges inherent in the process, hindering their ability to achieve their goals. The book, Utilizing Emotional Experience for Best Learning Design Practices, draws on extensive research and practical experience to explore many different perspectives on this issue. It argues that the emotional experience of learners must be considered throughout the design of educational models, tools, and programs, and it provides theoretical and applied insights for integrating emotional learning goals and strategies into instructional design, enabling educators to create more supportive and effective learning environments. By bridging the gap between theory and practice, this book empowers learning professionals to enhance the emotional experiences of adult learners and improve their overall outcomes. Through a nuanced exploration of emotional foundations, theoretical frameworks, and practical strategies, it equips educators with the tools to address the affective needs of learners. Utilizing Emotional Experience for Best Learning Design Practices is a vital resource for transforming adult education, fostering a more holistic and empowering approach to learning and development. |
| eda for sentiment analysis: Computational Data and Social Networks Andrea Tagarelli, Hanghang Tong, 2019-11-11 This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; Influence Modeling, Propagation, and Maximization; NLP and Affective Computing; Computational Methods for Social Good; and User Profiling and Behavior Modeling. |
| eda for sentiment analysis: Advanced Computing and Intelligent Technologies Rabindra Nath Shaw, |
| eda for sentiment analysis: Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics Khosrow-Pour, D.B.A., Mehdi, 2018-10-19 From cloud computing to data analytics, society stores vast supplies of information through wireless networks and mobile computing. As organizations are becoming increasingly more wireless, ensuring the security and seamless function of electronic gadgets while creating a strong network is imperative. Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics highlights the challenges associated with creating a strong network architecture in a perpetually online society. Readers will learn various methods in building a seamless mobile computing option and the most effective means of analyzing big data. This book is an important resource for information technology professionals, software developers, data analysts, graduate-level students, researchers, computer engineers, and IT specialists seeking modern information on emerging methods in data mining, information technology, and wireless networks. |
| eda for sentiment analysis: Mastering Spark with R Javier Luraschi, Kevin Kuo, Edgar Ruiz, 2019-10-07 If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions |
| eda for sentiment analysis: Emergent Converging Technologies and Biomedical Systems N. Marriwala, C. C. Tripathi, Shruti Jain, Shivakumar Mathapathi, 2022-03-23 The book contains peer-reviewed proceedings of the International Conference on Emergent Converging Technologies and Biomedical Systems 2021. It includes papers on wireless multimedia networks, green wireless networks, electric vehicles, biomedical signal processing and instrumentation, wearable sensors for health care monitoring, biomedical imaging, & bio-materials, modeling and simulation in medicine biomedical and health informatics. The book will serve as a useful guide for educators, researchers, and developers working in the area of signal processing, imaging, computing, instrumentation, artificial intelligence, and their related applications. This book will also provide support and aid to the researchers involved in designing the latest advancements in healthcare technologies. |
| eda for sentiment analysis: Machine Learning: ECML 2004 Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, Dino Pedreschi, 2004-11-05 The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers). |
| eda for sentiment analysis: Applied Text Analysis with Python Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda, 2018-06-11 From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity |
| eda for sentiment analysis: Network Security Through Data Analysis Michael Collins, 2017-09-08 Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In the updated second edition of this practical guide, security researcher Michael Collins shows InfoSec personnel the latest techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to harden and defend the systems within it. In three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. New chapters focus on active monitoring and traffic manipulation, insider threat detection, data mining, regression and machine learning, and other topics. You’ll learn how to: Use sensors to collect network, service, host, and active domain data Work with the SiLK toolset, Python, and other tools and techniques for manipulating data you collect Detect unusual phenomena through exploratory data analysis (EDA), using visualization and mathematical techniques Analyze text data, traffic behavior, and communications mistakes Identify significant structures in your network with graph analysis Examine insider threat data and acquire threat intelligence Map your network and identify significant hosts within it Work with operations to develop defenses and analysis techniques |
| eda for sentiment analysis: Machine Learning Techniques for Text Nikos Tsourakis, 2022-10-31 Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guide Key FeaturesLearn how to acquire and process textual data and visualize the key findingsObtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffsImplement models for solving real-world problems and evaluate their performanceBook Description With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation. What you will learnUnderstand fundamental concepts of machine learning for textDiscover how text data can be represented and build language modelsPerform exploratory data analysis on text corporaUse text preprocessing techniques and understand their trade-offsApply dimensionality reduction for visualization and classificationIncorporate and fine-tune algorithms and models for machine learningEvaluate the performance of the implemented systemsKnow the tools for retrieving text data and visualizing the machine learning workflowWho this book is for This book is for professionals in the area of computer science, programming, data science, informatics, business analytics, statistics, language technology, and more who aim for a gentle career shift in machine learning for text. Students in relevant disciplines that seek a textbook in the field will benefit from the practical aspects of the content and how the theory is presented. Finally, professors teaching a similar course will be able to pick pertinent topics in terms of content and difficulty. Beginner-level knowledge of Python programming is needed to get started with this book. |
| eda for sentiment analysis: Artificial Neural Networks and Machine Learning – ICANN 2020 Igor Farkaš, Paolo Masulli, Stefan Wermter, 2020-10-17 The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic. |
| eda for sentiment analysis: Advancing Software Engineering Through AI, Federated Learning, and Large Language Models Sharma, Avinash Kumar, Chanderwal, Nitin, Prajapati, Amarjeet, Singh, Pancham, Kansal, Mrignainy, 2024-05-02 The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. |
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U.S. Economic Development Administration
To lead the federal economic development agenda by promoting innovation and competitiveness, preparing American regions for growth and success in the worldwide economy.
EDA Program List | U.S. Economic Development Administration
EDA made a Coal Communities Commitment, and allocated $300 million of this funding to ensure support for coal communities. Assistance to Coal Communities (ACC) EDA designates a …
Funding Opportunities - U.S. Economic Development Administration
In addition, EDA does not require applicants to submit a processing or other fee. EDA grants can only be obtained by following the procedures described in the Notices of Funding …
Contact Us | U.S. Economic Development Administration
EDA Headquarters & Offices U.S. Department of Commerce 1401 Constitution Avenue, NW Suite 71014 Washington ...
EDA Grant History List - U.S. Economic Development Administration
EDA announced 4 investments from March 7 - March 14, 2024, totaling $315,000 which is matched by $32,142 in local investments. These investments include the following: (1) …
Public Works | U.S. Economic Development Administration
EDA’s Public Works program helps distressed communities revitalize, expand, and upgrade their physical infrastructure. This program enables communities to attract new industry, encourage …
American Rescue Plan - U.S. Economic Development Administration
Under the American Rescue Plan, EDA was allocated $3 billion in supplemental funding to meet the urgent needs of American communities. With deep gratitude to its partners, industry, and …
About EDA - U.S. Economic Development Administration
EDA provides economic development financial assistance to communities so they can encourage innovation and entrepreneurship in a way that works best for them. Through its network of …
Find Grant Resources - U.S. Economic Development Administration
Managing Your EDA Grant; Research Reports and Publications; Tribal Community Support; EDA Grant History List; Performance; Indirect Costs; News. Press Releases; EDA Blog; News …
What Projects Does EDA Fund and Where? - eda.gov
EDA economic development grants help communities bolster job growth and innovation, build resilience, recover from natural disasters, and promote long-term prosperity. To explain the full …
