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| customer feedback sentiment analysis: Sentiment Analysis in Social Networks Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu, 2016-10-06 The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network analysis - Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network mining - Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics |
| customer feedback sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis. |
| customer feedback sentiment analysis: Sentiment Analysis and Opinion Mining Bing Liu, 2012 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 |
| customer feedback sentiment analysis: Artificial Intelligence Techniques for Advanced Computing Applications D. Jude Hemanth, G. Vadivu, M. Sangeetha, Valentina Emilia Balas, 2020-07-23 This book features a collection of high-quality research papers presented at the International Conference on Advanced Computing Technology (ICACT 2020), held at the SRM Institute of Science and Technology, Chennai, India, on 23–24 January 2020. It covers the areas of computational intelligence, artificial intelligence, machine learning, deep learning, big data, and applications of artificial intelligence in networking, IoT and bioinformatics |
| customer feedback sentiment analysis: Opinion Mining and Sentiment Analysis Bo Pang, Lillian Lee, 2008 This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. |
| customer feedback sentiment analysis: Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik, 2020-01-24 This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. |
| customer feedback sentiment analysis: Sentiment Analysis for Social Media Carlos A. Iglesias, Antonio Moreno, 2020-04-02 Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection. |
| customer feedback sentiment analysis: Sentiment Analysis and Knowledge Discovery in Contemporary Business Rajput, Dharmendra Singh, Thakur, Ramjeevan Singh, Basha, S. Muzamil, 2018-08-31 In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals who are using them to collect vital data. Sentiment Analysis and Knowledge Discovery in Contemporary Business is an essential reference source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. Featuring research on topics such as knowledge retrieval and knowledge updating, this book is ideally designed for business managers, academicians, business professionals, researchers, graduate-level students, and technology developers seeking current research on data collection and management to drive profit. |
| customer feedback sentiment analysis: Sentiment Analysis and its Application in Educational Data Mining Soni Sweta, |
| customer feedback sentiment analysis: Advances in Intelligent Data Analysis VI A. Fazel Famili, 2005-08-30 This book constitutes the refereed proceedings of the 6th International Conference on Intelligent Data Analysis, IDA 2005, held in Madrid, Spain in September 2005. The 46 revised papers presented together with two tutorials and two invited talks were carefully reviewed and selected from 184 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization. |
| customer feedback sentiment analysis: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines Management Association, Information Resources, 2022-06-10 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. |
| customer feedback 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. |
| customer feedback sentiment analysis: Handbook of Natural Language Processing Nitin Indurkhya, Fred J. Damerau, 2010-02-22 The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater |
| customer feedback sentiment analysis: Advances in Sentiment Analysis , 2024-01-10 This cutting-edge book brings together experts in the field to provide a multidimensional perspective on sentiment analysis, covering both foundational and advanced methodologies. Readers will gain insights into the latest natural language processing and machine learning techniques that power sentiment analysis, enabling the extraction of nuanced emotions from text. Key Features: •State-of-the-Art Techniques: Explore the most recent advancements in sentiment analysis, from deep learning approaches to sentiment lexicons and beyond. •Real-World Applications: Dive into a wide range of applications, including social media monitoring, customer feedback analysis, and sentiment-driven decision-making. •Cross-Disciplinary Insights: Understand how sentiment analysis influences and is influenced by fields such as marketing, psychology, and finance. •Ethical and Privacy Considerations: Delve into the ethical challenges and privacy concerns inherent to sentiment analysis, with discussions on responsible AI usage. •Future Directions: Get a glimpse into the future of sentiment analysis, with discussions on emerging trends and unresolved challenges. This book is an essential resource for researchers, practitioners, and students in fields like natural language processing, machine learning, and data science. Whether you’re interested in understanding customer sentiment, monitoring social media trends, or advancing the state of the art, this book will equip you with the knowledge and tools you need to navigate the complex landscape of sentiment analysis. |
| customer feedback 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 |
| customer feedback sentiment analysis: Data Dynamo: Unleashing the Power of Big Data Analytics Mothiram Rajasekaran, 2024-04-26 Mothiram Rajasekaran,Senior Solution Consultant, Cloudera, USA. |
| customer feedback sentiment analysis: International Conference on Computing, Communication, Electrical and Biomedical Systems Arulmurugan Ramu, Chow Chee Onn, M.G. Sumithra, 2022-02-28 This book presents selected papers from the International Conference on Computing, Communication, Electrical and Biomedical Systems (ICCCEBS 2021), held in March 2021 at KPR College of Engineering and Technology, Coimbatore, Tamil Nadu, India. The conference explores the interface between industry and real-time environments with newly developed techniques in computing and communications engineering. The papers describe results of conceptual, constructive, empirical, experimental, and theoretical work in areas of computing, communication, electrical, and biomedical systems. Contributors include academic scientists, researchers, industry representatives, postdoctoral fellows, and research scholars from around the world. |
| customer feedback sentiment analysis: ICCAP 2021 A Mohan, D. S. Vijayan, 2021-12-22 This proceeding constitutes the thoroughly refereed proceedings of the 1st International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8, 2021. This event was organized by the group of Professors in Chennai. The Conference aims to provide the opportunities for informal conversations, have proven to be of great interest to other scientists and analysts employing these mathematical sciences in their professional work in business, industry, and government. The Conference continues to promote better understanding of the roles of modern applied mathematics, combinatorics, and computer science to acquaint the investigator in each of these areas with the various techniques and algorithms which are available to assist in his or her research. We selected 257 papers were carefully reviewed and selected from 741 submissions. The presentations covered multiple research fields like Computer Science, Artificial Intelligence, internet technology, smart health care etc., brought the discussion on how to shape optimization methods around human and social needs. |
| customer feedback sentiment analysis: TEXT PROCESSING AND SENTIMENT ANALYSIS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI Vivian Siahaan, Rismon Hasiholan Sianipar, 2023-06-26 In this book, we explored a code implementation for sentiment analysis using machine learning models, including XGBoost, LightGBM, and LSTM. The code aimed to build, train, and evaluate these models on Twitter data to classify sentiments. Throughout the project, we gained insights into the key steps involved and observed the findings and functionalities of the code. Sentiment analysis is a vital task in natural language processing, and the code was to give a comprehensive approach to tackle it. The implementation began by checking if pre-trained models for XGBoost and LightGBM existed. If available, the models were loaded; otherwise, new models were built and trained. This approach allowed for reusability of trained models, saving time and effort in subsequent runs. Similarly, the code checked if preprocessed data for LSTM existed. If not, it performed tokenization and padding on the text data, splitting it into train, test, and validation sets. The preprocessed data was saved for future use. The code also provided a function to build and train the LSTM model. It defined the model architecture using the Keras Sequential API, incorporating layers like embedding, convolutional, max pooling, bidirectional LSTM, dropout, and dense output. The model was compiled with appropriate loss and optimization functions. Training was carried out, with early stopping implemented to prevent overfitting. After training, the model summary was printed, and both the model and training history were saved for future reference. The train_lstm function ensured that the LSTM model was ready for prediction by checking the existence of preprocessed data and trained models. If necessary, it performed the required preprocessing and model building steps. The pred_lstm() function was responsible for loading the LSTM model and generating predictions for the test data. The function returned the predicted sentiment labels, allowing for further analysis and evaluation. To facilitate user interaction, the code included a functionality to choose the LSTM model for prediction. The choose_prediction_lstm() function was triggered when the user selected the LSTM option from a dropdown menu. It called the pred_lstm() function, performed evaluation tasks, and visualized the results. Confusion matrices and true vs. predicted value plots were generated to assess the model's performance. Additionally, the loss and accuracy history from training were plotted, providing insights into the model's learning process. In conclusion, this project provided a comprehensive overview of sentiment analysis using machine learning models. The code implementation showcased the steps involved in building, training, and evaluating models like XGBoost, LightGBM, and LSTM. It emphasized the importance of data preprocessing, model building, and evaluation in sentiment analysis tasks. The code also demonstrated functionalities for reusing pre-trained models and saving preprocessed data, enhancing efficiency and ease of use. Through visualization techniques, such as confusion matrices and accuracy/loss curves, the code enabled a better understanding of the model's performance and learning dynamics. Overall, this project highlighted the practical aspects of sentiment analysis and illustrated how different machine learning models can be employed to tackle this task effectively. |
| customer feedback sentiment analysis: AI-Driven Marketing Research and Data Analytics Masengu, Reason, Chiwaridzo, Option Takunda, Dube, Mercy, Ruzive, Benson, 2024-04-22 The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age. |
| customer feedback sentiment analysis: A First Course in Artificial Intelligence Osondu Oguike, 2021-07-14 The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence. |
| customer feedback sentiment analysis: Impact of AI and Tech-Driven Solutions in Hospitality and Tourism Talukder, Mohammad Badruddoza, Kumar, Sanjeev, Tyagi, Pankaj Kumar, 2024-08-14 With today’s rapidly evolving digital landscape, businesses in the hospitality and tourism industries face the challenge of harnessing the power of emerging technologies to stay competitive and meet the evolving needs of travelers. From personalized guest experiences to efficient operations and sustainable practices, there is a growing demand for innovative solutions that leverage artificial intelligence (AI) and cutting-edge technology. However, navigating this technological landscape can be daunting, with businesses often grappling with questions of implementation, integration, and ethical considerations. Impact of AI and Tech-Driven Solutions in Hospitality and Tourism emerges to provide insight and guidance in addressing these challenges. By delving into the transformative potential of AI and technology, this book offers a comprehensive exploration of how these innovations are reshaping the hospitality and tourism sectors. Through in-depth analyses, case studies, and practical examples, readers gain valuable insights into the myriad ways in which AI-driven solutions can enhance guest experiences, streamline operations, and drive sustainable practices within the industry. |
| customer feedback sentiment analysis: AI-Based Data Analytics Kiran Chaudhary, Mansaf Alam, 2023-12-29 This book covers various topics related to marketing and business analytics. It explores how organizations can increase their profits by making better decisions in a timely manner through the use of data analytics. This book is meant for students, practitioners, industry professionals, researchers, and academics working in the field of commerce and marketing, big data analytics, and organizational decision-making. Highlights of the book include: The role of Explainable AI in improving customer experiences in e-commerce Sentiment analysis of social media Data analytics in business intelligence Federated learning for business intelligence AI-based planning of business management An AI-based business model innovation in new technologies An analysis of social media marketing and online impulse buying behaviour AI-Based Data Analytics: Applications for Business Management has two primary focuses. The first is on analytics for decision-making and covers big data analytics for market intelligence, data analytics and consumer behavior, and the role of big data analytics in organizational decision-making. The book’s second focus is on digital marketing and includes the prediction of marketing by consumer analytics, web analytics for digital marketing, smart retailing, and leveraging web analytics for optimizing digital marketing strategies. |
| customer feedback sentiment analysis: Smart Systems: Innovations in Computing Arun K. Somani, |
| customer feedback sentiment analysis: Becoming an AI expert Cybellium Ltd, 2023-09-05 In a world driven by cutting-edge technology, artificial intelligence (AI) stands at the forefront of innovation. Becoming an AI Expert is an illuminating guide that takes readers on a transformative journey, equipping them with the knowledge and skills needed to navigate the dynamic realm of AI and emerge as true experts in the field. About the Book: In this comprehensive handbook, readers will embark on a captivating exploration of AI from its foundational concepts to advanced applications. Authored by leading experts, Becoming an AI Expert offers a structured approach to mastering the intricacies of AI, making it an invaluable resource for both novices and aspiring professionals. Key Features: · AI Fundamentals: The book starts with a solid introduction to AI, demystifying complex concepts and terminology. Readers will gain a clear understanding of the building blocks that underpin AI technologies. · Hands-On Learning: Through practical examples, coding exercises, and real-world projects, readers will engage in hands-on learning that deepens their understanding of AI techniques and algorithms. · Problem-Solving Approach: Becoming an AI Expert encourages a problem-solving mindset, guiding readers through the process of identifying challenges that AI can address and devising effective solutions. · AI Subfields: From machine learning and deep learning to natural language processing and computer vision, the book provides an overview of key AI subfields, allowing readers to explore specialized areas of interest. · Ethical Considerations: As AI increasingly shapes society, ethical considerations become paramount. The book delves into the ethical implications of AI and equips readers with tools to develop responsible and socially conscious AI solutions. · Cutting-Edge Trends: Readers will stay ahead of the curve by exploring emerging trends such as AI in healthcare, autonomous vehicles, and AI ethics, ensuring they remain at the forefront of AI advancements. · Industry Insights: Featuring interviews and case studies from AI practitioners, Becoming an AI Expert offers a glimpse into real-world applications and insights, bridging the gap between theory and practice. Who Should Read This Book: Becoming an AI Expert is an essential read for students, professionals, and enthusiasts seeking to build a solid foundation in AI or advance their existing knowledge. Whether you're a computer science student, a software developer, an engineer, or a curious individual passionate about AI, this book serves as a comprehensive guide to becoming proficient in the AI landscape. About the Authors: The authors of Becoming an AI Expert are distinguished experts in the field of artificial intelligence. With years of research, industry experience, and academic contributions, they bring a wealth of knowledge to this guide. Their collective expertise ensures that readers receive accurate, up-to-date, and insightful information about AI. |
| customer feedback sentiment analysis: How to Start a Business as a Social Media Analytics Consultant AS, How to Start a Business About the Book: Unlock the essential steps to launching and managing a successful business with How to Start a Business books. Part of the acclaimed How to Start a Business series, this volume provides tailored insights and expert advice specific to the industry, helping you navigate the unique challenges and seize the opportunities within this field. What You'll Learn Industry Insights: Understand the market, including key trends, consumer demands, and competitive dynamics. Learn how to conduct market research, analyze data, and identify emerging opportunities for growth that can set your business apart from the competition. Startup Essentials: Develop a comprehensive business plan that outlines your vision, mission, and strategic goals. Learn how to secure the necessary financing through loans, investors, or crowdfunding, and discover best practices for effectively setting up your operation, including choosing the right location, procuring equipment, and hiring a skilled team. Operational Strategies: Master the day-to-day management of your business by implementing efficient processes and systems. Learn techniques for inventory management, staff training, and customer service excellence. Discover effective marketing strategies to attract and retain customers, including digital marketing, social media engagement, and local advertising. Gain insights into financial management, including budgeting, cost control, and pricing strategies to optimize profitability and ensure long-term sustainability. Legal and Compliance: Navigate regulatory requirements and ensure compliance with industry laws through the ideas presented. Why Choose How to Start a Business books? Whether you're wondering how to start a business in the industry or looking to enhance your current operations, How to Start a Business books is your ultimate resource. This book equips you with the knowledge and tools to overcome challenges and achieve long-term success, making it an invaluable part of the How to Start a Business collection. Who Should Read This Book? Aspiring Entrepreneurs: Individuals looking to start their own business. This book offers step-by-step guidance from idea conception to the grand opening, providing the confidence and know-how to get started. Current Business Owners: Entrepreneurs seeking to refine their strategies and expand their presence in the sector. Gain new insights and innovative approaches to enhance your current operations and drive growth. Industry Professionals: Professionals wanting to deepen their understanding of trends and best practices in the business field. Stay ahead in your career by mastering the latest industry developments and operational techniques. Side Income Seekers: Individuals looking for the knowledge to make extra income through a business venture. Learn how to efficiently manage a part-time business that complements your primary source of income and leverages your skills and interests. Start Your Journey Today! Empower yourself with the insights and strategies needed to build and sustain a thriving business. Whether driven by passion or opportunity, How to Start a Business offers the roadmap to turning your entrepreneurial dreams into reality. Download your copy now and take the first step towards becoming a successful entrepreneur! Discover more titles in the How to Start a Business series: Explore our other volumes, each focusing on different fields, to gain comprehensive knowledge and succeed in your chosen industry. |
| customer feedback sentiment analysis: Soft Computing: Theories and Applications Rajesh Kumar, |
| customer feedback sentiment analysis: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| customer feedback sentiment analysis: The Text Mining Handbook Ronen Feldman, James Sanger, 2007 Publisher description |
| customer feedback sentiment analysis: Artificial Intelligence and Human Performance in Transportation Dimitrios Ziakkas, Anastasios Plioutsias, 2024-10-30 Artificial Intelligence (AI) is a major technological advancement in the 21st century. With its influence spreading to all aspects of our lives and the engineering sector, establishing well-defined objectives is crucial for successfully integrating AI in the field of transportation. This book presents different ways of adopting emerging technologies in transportation operations, including security, safety, online training, and autonomous vehicle operations on land, sea, and air. This guide is a dynamic resource for senior management and decision-makers, with essential practical advice distilled from the expertise of specialists in the field. It addresses the most critical issues facing transportation service providers in adopting AI and investigates the relationship between the human operator and the technology to navigate what is and is not feasible or impossible. Case studies of actual implementation provide context to common scenarios in the transportation sector. This book will serve the reader as the starting point for practical questions regarding the deployment and safety assurance of new and emergent technologies in the transportation domains. Artificial Intelligence and Human Performance in Transportation is a beneficial read for professionals in the fields of Human Factors, Engineering (Aviation, Maritime and Land), Logistics, Manufacturing, Accident Investigation and Safety, Cybersecurity and Human Resources. |
| customer feedback sentiment analysis: Money-Making Magic: Cracking the Code to Customer Needs Shu Chen Hou, Unlock the Secret to Financial Success with Money-Making Magic Are you ready to transform your business and skyrocket your revenue? In the dynamic world of entrepreneurship, the key to lasting success is understanding and meeting the needs of your customers. Money-Making Magic: Cracking the Code to Customer Needs is your ultimate guide to achieving unprecedented financial prosperity by putting your customers at the heart of your business. This groundbreaking book takes you on a journey through the art and science of customer-centric wealth creation. From the very basics of building strong customer relationships to advanced strategies that will set you apart in your industry, this comprehensive guide leaves no stone unturned. Discover the magic of: 🌟 Creating Genuine Value: Learn how to go beyond transactional interactions and forge deep connections with your customers. Understand what truly matters to them and deliver unparalleled value. 🌟 Mastering Customer Loyalty: Cultivate unwavering loyalty that transcends price points and competition. Explore the psychology behind customer behavior and uncover the secrets to long-term relationships. 🌟 Network Marketing Excellence: Leverage the power of networking and network marketing to expand your reach and influence. Turn connections into thriving revenue streams. 🌟 Digital Marketing Domination: Harness the digital landscape to maximize your online presence. Craft compelling content, employ cutting-edge digital strategies, and watch your business flourish in the digital age. 🌟 Persuasive Communication: Master the art of persuasion ethically, winning over customers with effective communication that resonates and convinces. 🌟 Global Expansion: Explore the possibilities of taking your business global and tapping into new markets. Adapt and thrive in an ever-evolving global economy. Money-Making Magic isn't just another business book; it's your roadmap to financial prosperity. Whether you're an aspiring entrepreneur or a seasoned business owner, this book equips you with the tools, insights, and strategies to turn customer needs into boundless opportunities. Don't miss out on this transformative journey to unlock the true magic of financial success. Your business deserves to thrive, and Money-Making Magic is your key to making it happen. Get your copy today and start your journey towards unparalleled wealth and success. Prepare to be spellbound by the possibilities, as you witness the money-making magic unfold before your eyes. |
| customer feedback sentiment analysis: International Conference on Innovative Computing and Communications Aboul Ella Hassanien, Oscar Castillo, Sameer Anand, Ajay Jaiswal, 2023-07-31 This book includes high-quality research papers presented at the Sixth International Conference on Innovative Computing and Communication (ICICC 2023), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on February 17–18, 2023. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications. |
| customer feedback sentiment analysis: Toward Artificial General Intelligence Victor Hugo C. de Albuquerque, Pethuru Raj, Satya Prakash Yadav, 2023-11-06 |
| customer feedback sentiment analysis: LLMs Ronald Legarski, 2024-09-01 LLMs: From Origin to Present and Future Applications by Ronald Legarski is an authoritative exploration of Large Language Models (LLMs) and their profound impact on artificial intelligence, machine learning, and various industries. This comprehensive guide traces the evolution of LLMs from their early beginnings to their current applications, and looks ahead to their future potential across diverse fields. Drawing on extensive research and industry expertise, Ronald Legarski provides readers with a detailed understanding of how LLMs have developed, the technologies that power them, and the transformative possibilities they offer. This book is an invaluable resource for AI professionals, researchers, and enthusiasts who want to grasp the intricacies of LLMs and their applications in the modern world. Key topics include: The Origins of LLMs: A historical perspective on the development of natural language processing and the key milestones that led to the creation of LLMs. Technological Foundations: An in-depth look at the architecture, data processing, and training techniques that underpin LLMs, including transformer models, tokenization, and attention mechanisms. Current Applications: Exploration of how LLMs are being used today in industries such as healthcare, legal services, education, content creation, and more. Ethical Considerations: A discussion on the ethical challenges and societal impacts of deploying LLMs, including bias, fairness, and the need for responsible AI governance. Future Directions: Insights into the future of LLMs, including their role in emerging technologies, interdisciplinary research, and the potential for creating more advanced AI systems. With clear explanations, practical examples, and forward-thinking perspectives, LLMs: From Origin to Present and Future Applications equips readers with the knowledge to navigate the rapidly evolving field of AI. Whether you are a seasoned AI professional, a researcher in the field, or someone with an interest in the future of technology, this book offers a thorough exploration of LLMs and their significance in the digital age. Discover how LLMs are reshaping industries, driving innovation, and what the future holds for these powerful AI models. |
| customer feedback sentiment analysis: Big Data Analytics Techniques for Market Intelligence Darwish, Dina, 2024-01-04 The ever-expanding realm of Big Data poses a formidable challenge for academic scholars and professionals due to the sheer magnitude and diversity of data types, along with the continuous influx of information from various sources. Extracting valuable insights from this vast and complex dataset is crucial for organizations to uncover market intelligence and make informed decisions. However, without the proper guidance and understanding of Big Data analytics techniques and methodologies, scholars may struggle to navigate this landscape and maximize the potential benefits of their research. In response to this pressing need, Professor Dina Darwish presents Big Data Analytics Techniques for Market Intelligence, a groundbreaking book that addresses the specific challenges faced by scholars and professionals in the field. Through a comprehensive exploration of various techniques and methodologies, this book offers a solution to the hurdles encountered in extracting meaningful information from Big Data. Covering the entire lifecycle of Big Data analytics, including preprocessing, analysis, visualization, and utilization of results, the book equips readers with the knowledge and tools necessary to unlock the power of Big Data and generate valuable market intelligence. With real-world case studies and a focus on practical guidance, scholars and professionals can effectively leverage Big Data analytics to drive strategic decision-making and stay at the forefront of this rapidly evolving field. |
| customer feedback sentiment analysis: Python Data Analysis Avinash Navlani, Armando Fandango, Ivan Idris, 2021-02-05 Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book. |
| customer feedback sentiment analysis: AI-Powered Productivity Dr. Asma Asfour, 2024-07-29 This book, AI-Powered Productivity, aims to provide a guide to understanding, utilizing AI and generative tools in various professional settings. The primary purpose of this book is to offer readers a deep dive into the concepts, tools, and practices that define the current AI landscape. From foundational principles to advanced applications, this book is structured to cater to both beginners and professionals looking to enhance their knowledge and skills in AI. This book is divided into nine chapters, each focusing on a specific aspect of AI and its practical applications: Chapter 1 introduces the basic concepts of AI, its impact on various sectors, and key factors driving its rapid advancement, along with an overview of generative AI tools. Chapter 2 delves into large language models like ChatGPT, Google Gemini, Claude, Microsoft's Turing NLG, and Facebook's BlenderBot, exploring their integration with multimodal technologies and their effects on professional productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, including tutorials on crafting effective prompts and advanced techniques, as well as real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 addresses data-driven decision- making, covering data analysis techniques, AI in trend identification, consumer behavior analysis, strategic planning, and product development. Chapter 6 discusses strategic and ethical considerations of AI, including AI feasibility, tool selection, multimodal workflows, and best practices for ethical AI development and deployment. Chapter 7 highlights the role of AI in transforming training and professional development, covering structured training programs, continuous learning initiatives, and fostering a culture of innovation and experimentation. Chapter 8 provides a guide to successfully implementing AI in organizations, discussing team composition, collaborative approaches, iterative development processes, and strategic alignment for AI initiatives. Finally, Chapter 9 looks ahead to the future of work, preparing readers for the AI revolution by addressing training and education, career paths, common fears, and future trends in the workforce. The primary audience for the book is professionals seeking to enhance productivity and organizations or businesses. For professionals, the book targets individuals from various industries, reflecting its aim to reach a broad audience across different professional fields. It is designed for employees at all levels, offering valuable insights to both newcomers to AI and seasoned professionals. Covering a range of topics from foundational concepts to advanced applications, the book is particularly relevant for those interested in improving efficiency, with a strong emphasis on practical applications and productivity tools to optimize work processes. For organizations and businesses, the book serves as a valuable resource for decision-makers and managers, especially with chapters on data-driven decision-making, strategic considerations, and AI implementation. HR and training professionals will find the focus on AI in training and development beneficial for talent management, while IT and technology teams will appreciate the information on AI tools and concepts. |
| customer feedback sentiment analysis: Advances in Information Retrieval Allan Hanbury, Gabriella Kazai, Andreas Rauber, Norbert Fuhr, 2015-03-16 This book constitutes the proceedings of the 37th European Conference on IR Research, ECIR 2015, held in Vienna, Austria, in March/April 2015. The 44 full papers, 41 poster papers and 7 demonstrations presented together with 3 keynotes in this volume were carefully reviewed and selected from 305 submissions. The focus of the papers were on following topics: aggregated search and diversity, classification, cross-lingual and discourse, efficiency, evaluation, event mining and summarisation, information extraction, recommender systems, semantic and graph-based models, sentiment and opinion, social media, specific search tasks, temporal models and features, topic and document models, user behavior and reproducible IR. |
| customer feedback sentiment analysis: Data Engineering and Communication Technology K. Srujan Raju, Roman Senkerik, Satya Prasad Lanka, V. Rajagopal, 2020-01-08 This book includes selected papers presented at the 3rd International Conference on Data Engineering and Communication Technology (ICDECT-2K19), held at Stanley College of Engineering and Technology for Women, Hyderabad, from 15 to 16 March 2019. It features advanced, multidisciplinary research towards the design of smart computing, information systems, and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence, and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment, and industry. |
| customer feedback sentiment analysis: New Opportunities for Sentiment Analysis and Information Processing Sharaff, Aakanksha, Sinha, G. R., Bhatia, Surbhi, 2021-06-25 Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students. |
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