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| call center 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. |
| call center sentiment analysis: Non-Linguistic Analysis of Call Center Conversations Sunil Kumar Kopparapu, 2014-08-02 The book focuses on the part of the audio conversation not related to language such as speaking rate (in terms of number of syllables per unit time) and emotion centric features. This text examines using non-linguistics features to infer information from phone calls to call centers. The author analyzes how the conversation happens and not what the conversation is about by audio signal processing and analysis. |
| call center sentiment analysis: Advice from a Call Center Geek Thomas Laird, 2018-08-21 Advice from a Call Center Geek: Rethinking Call Center Operations is a field manual for the 21st century contact center. Practical, poignant, and funny, Tom dishes out amazing real-world advice that has made his organization successful. From culture to education to incentives, Tom addresses the key areas to make your contact center world-class!Paul HerdmanHead of Customer ExperienceNICE inContactAdvice From a Call Center Geek takes a look at a new way of running today's high end contact center. Tom Laird, the CEO of award winning Expivia Interaction Marketing, 600 seat BPO call center guides you through the process of developing a world class operation.This book will take you through the process of evaluating and changing your call center's culture, how to look beyond a resume to hire the right associates and show you how to educate for quality while maintaining high level management. Advice from a Call Center Geek will make you rethink how the call center manager of today should be looking at running their call center. |
| call center 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. |
| call center sentiment analysis: Multi-Modal Sentiment Analysis Hua Xu, 2023-11-26 The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields. |
| call center 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 |
| call center sentiment analysis: Deep Data Analytics for New Product Development Walter R. Paczkowski, 2020-02-19 This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs. |
| call center 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. |
| call center sentiment analysis: Course ILT Course Technology, Inc, 2003-02-28 This ILT Series course give students an overview of inbound call centers, managerial roles, and technologies that affect call centers. The course teaches students how to establish a call center, identify the call center managers' typical responsibilities, and determine the necessary technologies needed to best serve the company's customers, identify customer expectations, reduce the percentage of lost calls, calculate staff levels, and identify the reports that are used to evaluate a call center's performance. Students will also learn about establishing service goals, identifying areas for attention, and communicating effectively with executives. Course activities also cover reducing turnover, training employees effectively, managing employee stress, motivating, and communicating with employees. Finally, students will learn how to evaluate employee performance and establish monitoring programs. The manual is designed for quick scanning in the classroom and filled with interactive exercises that help ensure student success. |
| call center sentiment analysis: Amazon Connect: Up and Running Jeff Armstrong, 2021-04-23 Explore Amazon Connect, from implementing call flows and creating AI bots to integrating artificial intelligence solutions and analyzing critical customer sentiment Key FeaturesDiscover how to integrate chat with Connect to allow organizations to reduce operations costsLeverage machine learning to perform natural language processing (NLP) for analyzing customer feedback and trendsLearn how to integrate your enterprise application with Amazon ConnectBook Description Amazon Connect is a pay-as-you-go cloud contact center solution that powers Amazon's customer contact system and provides an impressive user experience while reducing costs. Connect's scalability has been especially helpful during COVID-19, helping customers with research, remote work, and other solutions, and has driven adoption rates higher. Amazon Connect: Up and Running will help you develop a foundational understanding of Connect's capabilities and how businesses can effectively estimate the costs and risks associated with migration. Complete with hands-on tutorials, costing profiles, and real-world use cases relating to improving business operations, this easy-to-follow guide will teach you everything you need to get your call center online, interface with critical business systems, and take your customer experience to the next level. As you advance, you'll understand the benefits of using Amazon Connect and cost estimation guidelines for migration and new deployments. Later, the book guides you through creating AI bots, implementing interfaces, and leveraging machine learning for business analytics. By the end of this book, you'll be able to bring a Connect call center online with all its major components and interfaces to significantly reduce personnel overhead and provide your customers with an enhanced user experience (UX). What you will learnBecome well-versed with the capabilities and benefits of Amazon ConnectDetermine cost-effective solutions by integrating Connect with AWSCreate, modify, and connect contact flows to improve efficiencyBuild a conversational interface with Amazon LexFind out how to transfer contact records out of Connect via KinesisGather user insights and improve business operations with Amazon QuickSightAnalyze customer-agent conversations with ML speech analytics capabilitiesDiscover ways to provide superior customer service at a lower costWho this book is for This Amazon Connect book is for anyone looking to save costs and improve their customer experience through a more advanced call center using Amazon Connect and other AWS capabilities. A technical understanding of Amazon Web Services (AWS) and beginner-level business administration experience are necessary to address cost concerns and risks. |
| call center sentiment analysis: Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications Gary Miner, 2012-01-11 The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities-- |
| call center sentiment analysis: Intelligent Natural Language Processing: Trends and Applications Khaled Shaalan, Aboul Ella Hassanien, Fahmy Tolba, 2017-11-17 This book brings together scientists, researchers, practitioners, and students from academia and industry to present recent and ongoing research activities concerning the latest advances, techniques, and applications of natural language processing systems, and to promote the exchange of new ideas and lessons learned. Taken together, the chapters of this book provide a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of intelligent natural language processing. The book presents the state-of-the-art in research on natural language processing, computational linguistics, applied Arabic linguistics and related areas. New trends in natural language processing systems are rapidly emerging – and finding application in various domains including education, travel and tourism, and healthcare, among others. Many issues encountered during the development of these applications can be resolved by incorporating language technology solutions. The topics covered by the book include: Character and Speech Recognition; Morphological, Syntactic, and Semantic Processing; Information Extraction; Information Retrieval and Question Answering; Text Classification and Text Mining; Text Summarization; Sentiment Analysis; Machine Translation Building and Evaluating Linguistic Resources; and Intelligent Language Tutoring Systems. |
| call center sentiment analysis: An Introduction to Data Science With Python Jeffrey S. Saltz, Jeffrey M. Stanton, 2024-05-29 An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don′t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site. |
| call center sentiment analysis: Modern Generative AI with ChatGPT and OpenAI Models Valentina Alto, 2023-05-26 Harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore the theory behind generative AI models and the road to GPT3 and GPT4 Become familiar with ChatGPT's applications to boost everyday productivity Learn to embed OpenAI models into applications using lightweight frameworks like LangChain Book Description Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You'll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you'll explore use cases where ChatGPT can boost productivity and enhance creativity. You'll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You'll also discover enterprise-level scenarios that leverage OpenAI models' APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you'll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you'll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models' APIs in your own projects. What you will learn Understand generative AI concepts from basic to intermediate level Focus on the GPT architecture for generative AI models Maximize ChatGPT's value with an effective prompt design Explore applications and use cases of ChatGPT Use OpenAI models and features via API calls Build and deploy generative AI systems with Python Leverage Azure infrastructure for enterprise-level use cases Ensure responsible AI and ethics in generative AI systems Who this book is for This book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models. A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts. |
| call center sentiment analysis: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| call center sentiment analysis: Data Engineering with AWS Gareth Eagar, 2021-12-29 The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along. |
| call center sentiment analysis: Predictive Marketing Omer Artun, Dominique Levin, 2015-08-06 Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience. |
| call center sentiment analysis: Applied Insurance Analytics Patricia L. Saporito, 2015 Data is the insurance industry's single greatest asset. Yet many insurers radically underutilize their data assets, and are failing to fully leverage modern analytics. This makes them vulnerable to traditional and non-traditional competitors alike. Today, insurers largely apply analytics in important but stovepiped operational areas like underwriting, claims, marketing and risk management. By and large, they lack an enterprise analytic strategy -- or, if they have one, it is merely an architectural blueprint, inadequately business-driven or strategically aligned. Now, writing specifically for insurance industry professionals and leaders, Patricia Saporito uncovers immense new opportunities for driving competitive advantage from analytics -- and shows how to overcome the obstacles that stand in your way. Drawing on 25+ years of insurance industry experience, Saporito introduces proven best practices for developing, maturing, and profiting from your analytic capabilities. This user-friendly handbook advocates an enterprise strategy approach to analytics, presenting a common framework you can quickly adapt based on your unique business model and current capabilities. Saporito reviews common analytic applications by functional area, offering specific case studies and examples, and helping you build upon the analytics you're already doing. She presents data governance models and models proven to help you organize and deliver trusted data far more effectively. Finally, she provides tools and frameworks for improving the analytic IQ of your entire enterprise, from IT developers to business users. |
| call center 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. |
| call center 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. |
| call center sentiment analysis: The AI Revolution in Customer Service and Support Ross Smith, Mayte Cubino, Emily McKeon, 2024-07-16 In the rapidly evolving AI landscape, customer service and support professionals find themselves in a prime position to take advantage of this innovative technology to drive customer success. The AI Revolution in Customer Service and Support is a practical guide for professionals who want to harness the power of generative AI within their organizations to create more powerful customer and employee experiences. This book is designed to equip you with the knowledge and confidence to embrace the AI revolution and integrate the technology, such as large language models (LLMs), machine learning, predictive analytics, and gamified learning, into the customer experience. Start your journey toward leveraging this technology effectively to optimize organizational productivity. A portion of the book’s proceeds will be donated to the nonprofit Future World Alliance, dedicated to K-12 AI ethics education. IN THIS BOOK YOU’LL LEARN About AI, machine learning, and data science How to develop an AI vision for your organization How and where to incorporate AI technology in your customer experience fl ow About new roles and responsibilities for your organization How to improve customer experience while optimizing productivity How to implement responsible AI practices How to strengthen your culture across all generations in the workplace How to address concerns and build strategies for reskilling and upskilling your people How to incorporate games, play, and other techniques to engage your agents with AI Explore thought experiments for the future of support in your organization “Insightful & comprehensive—if you run a service & support operation, put this book on your essential reading list right now!” —PHIL WOLFENDEN, Cisco, VP, Customer Experience “This book is both timely and relevant as we enter an unprecedented period in our industry and the broader world driven by Generative AI. The magnitude and speed of change we’re experiencing is astounding and this book does an outstanding job balancing technical knowledge with the people and ethical considerations we must also keep front of mind.” —BRYAN BELMONT, Microsoft, Corporate VP, Customer Service & Support “The authors of this book are undoubtedly on the front lines of operationalizing Gen AI implementations in customer support environments... and they know undoubtedly that at its core, support is about people and genuine human connections. This book walks you through their journey to keep people at the center of this technical tsunami.” —PHAEDRA BOINODIRIS, Author, AI for the Rest of Us |
| call center sentiment analysis: Getting Started with Business Analytics David Roi Hardoon, Galit Shmueli, 2013-03-26 Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization. |
| call center sentiment analysis: Digital TV and Wireless Multimedia Communications Guangtao Zhai, Jun Zhou, Hua Yang, Ping An, Xiaokang Yang, 2022-04-16 This book presents revised selected papers from the 18th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2021, held in Shanghai, China, in December 2021. The 41 papers presented in this volume were carefully reviewed and selected from 110 submissions. They were organized in topical sections on image analysis; quality assessment; target detection; video processing; big data. |
| call center sentiment analysis: Deep Learning Concepts in Operations Research Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar, Aryan Chaudhary, 2024-08-30 The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. |
| call center sentiment analysis: Data Science for Business With R Jeffrey S. Saltz, Jeffrey M. Stanton, 2021-02-03 Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field. |
| call center sentiment analysis: Win with Advanced Business Analytics Jean-Paul Isson, Jesse Harriott, 2012-09-25 Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition. |
| call center sentiment analysis: AI - The new intelligence in sales Livia Rainsberger, 2022-09-26 This book offers sales managers a quick overview of the possible applications of artificial intelligence in sales and explains basic functionalities. What is behind terms such as Sales Automation, Sales AI Analytics, Sales Enablement, Conversational AI, Lead Intelligence, Dynamic Pricing, Sales Management Intelligence and many more? Where is the concrete potential for sales organizations? And how will AI change the work in sales? The author presents the AI tools available on the market today and their application and describes the advantages and disadvantages as well as the limits and possibilities using clear examples. Executives in marketing and sales as well as entrepreneurs and managing directors, especially in medium-sized companies, will receive answers to the most important questions and additionally concrete recommendations for action for the implementation in their own companies. |
| call center sentiment analysis: Unstructured Data Analytics Jean Paul Isson, 2018-03-02 Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis. |
| call center sentiment analysis: Real-World Data Mining Dursun Delen, 2014-12-16 Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance. Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based. |
| call center sentiment analysis: Computational Linguistics and Intelligent Text Processing Alexander Gelbukh, 2013-03-12 This two-volume set, consisting of LNCS 7816 and LNCS 7817, constitutes the thoroughly refereed proceedings of the 13th International Conference on Computer Linguistics and Intelligent Processing, CICLING 2013, held on Samos, Greece, in March 2013. The total of 91 contributions presented was carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections named: general techniques; lexical resources; morphology and tokenization; syntax and named entity recognition; word sense disambiguation and coreference resolution; semantics and discourse; sentiment, polarity, subjectivity, and opinion; machine translation and multilingualism; text mining, information extraction, and information retrieval; text summarization; stylometry and text simplification; and applications. |
| call center sentiment analysis: The Big Book of Dashboards Steve Wexler, Jeffrey Shaffer, Andy Cotgreave, 2017-04-24 The definitive reference book with real-world solutions you won't find anywhere else The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards. Comprising dozens of examples that address different industries and departments (healthcare, transportation, finance, human resources, marketing, customer service, sports, etc.) and different platforms (print, desktop, tablet, smartphone, and conference room display) The Big Book of Dashboards is the only book that matches great dashboards with real-world business scenarios. By organizing the book based on these scenarios and offering practical and effective visualization examples, The Big Book of Dashboards will be the trusted resource that you open when you need to build an effective business dashboard. In addition to the scenarios there's an entire section of the book that is devoted to addressing many practical and psychological factors you will encounter in your work. It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts? The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world. A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage. |
| call center sentiment analysis: Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace Babulak, Eduard, 2024-06-04 In the rapidly transforming landscape of fast-paced technology evolution, the fusion of artificial intelligence (AI) and humanoid robotics is set to redefine academia as we know it. From advancements in AI, humanoid robotics, nano and bio technologies, and smart medicine, the vision of an ultra-smart cyberspace is becoming a tangible reality. Yet, amid this transformative potential, scholars face a pressing challenge how to navigate the complexities of these cutting-edge technologies to drive impactful research and innovation. Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace beckons scholars to harness the full potential of applied AI and humanoid robotics in academia. This book illuminates the most effective applications of these technologies across various disciplines such as industry, business, health, government, military, and critical cyber infrastructure. Through rigorously peer-reviewed chapters, the book addresses key issues, provides technical solutions, and guides future research directions, fostering a collaborative bridge between academia and industry. |
| call center sentiment analysis: Critical Conversation Analysis Hansun Zhang Waring, Nadja Tadic, 2024-05-14 This book presents the first collection of conversation analytic studies addressed exclusively to issues of inequality and injustice. It offers a broad depiction of how inequality and injustice are reproduced, resisted and transformed in our daily life; together the chapters produce a forensic analysis of how participants enact discriminatory ideologies, negotiate systemic power imbalances, and pursue social change in and through the nuances of their interactions. The authors draw on audio and video recordings of interaction in a wide range of social settings, ranging from classrooms to family dinners, and political town halls to television sitcoms. The book demonstrates the power of conversation analysis to tackle issues of social (in)justice and (in)equality and launches critical conversation analysis as a distinct empirical program dedicated to systematically investigating and promoting inclusion and equity in the minute details of everyday interaction. |
| call center sentiment analysis: Big Data with Hadoop MapReduce Rathinaraja Jeyaraj, Ganeshkumar Pugalendhi, Anand Paul, 2020-05-01 The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to: • understand what big data is and the factors that are involved • understand the inner workings of MapReduce, which is essential for certification exams • learn the features and weaknesses of MapReduce • set up Hadoop clusters with 100s of physical/virtual machines • create a virtual machine in AWS • write MapReduce with Eclipse in a simple way • understand other big data processing tools and their applications |
| call center sentiment analysis: Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media Keikhosrokiani, Pantea, Pourya Asl, Moussa, 2022-02-18 Opinion mining and text analytics are used widely across numerous disciplines and fields in today’s society to provide insight into people’s thoughts, feelings, and stances. This data is incredibly valuable and can be utilized for a range of purposes. As such, an in-depth look into how opinion mining and text analytics correlate with social media and literature is necessary to better understand audiences. The Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics applied to opinion mining and text analytics on literary works and social media. It also focuses on theories, methods, and approaches in which data analysis techniques can be used to analyze data to provide a meaningful pattern. Covering a wide range of topics such as sentiment analysis and stance detection, this publication is ideal for lecturers, researchers, academicians, practitioners, and students. |
| call center sentiment analysis: Advances in Knowledge Discovery and Data Mining Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda, 2015-05-08 This two-volume set, LNAI 9077 + 9078, constitutes the refereed proceedings of the 19th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2015, held in Ho Chi Minh City, Vietnam, in May 2015. The proceedings contain 117 paper carefully reviewed and selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional, and sequential data; entity resolution and topic-modeling; itemset and high-performance data mining; and recommendations. |
| call center sentiment analysis: Proceedings of International Conference on Recent Innovations in Computing Yashwant Singh, Pradeep Kumar Singh, Maheshkumar H. Kolekar, Arpan Kumar Kar, Paulo J. Sequeira Gonçalves, 2023-05-02 This book features selected papers presented at the 5th International Conference on Recent Innovations in Computing (ICRIC 2022), held on May 13–14, 2022, at the Central University of Jammu, India, and organized by the university’s Department of Computer Science and Information Technology. The conference was hosted in association with ELTE, Hungary; Knowledge University, Erbil; Cyber Security Research Lab and many other national & international partners. The book is divided into two volumes, and it includes the latest research in the areas of software engineering, cloud computing, computer networks and Internet technologies, artificial intelligence, information security, database and distributed computing, and digital India. |
| call center sentiment analysis: Natural Language Processing with AWS AI Services Mona M, Premkumar Rangarajan, Julien Simon, 2021-11-26 Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services Key FeaturesGet to grips with AWS AI services for NLP and find out how to use them to gain strategic insightsRun Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomesUnderstand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2IBook Description Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today's business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications. What you will learnAutomate various NLP workflows on AWS to accelerate business outcomesUse Amazon Textract for text, tables, and handwriting recognition from images and PDF filesGain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon ComprehendSet up end-to-end document processing pipelines to understand the role of humans in the loopDevelop NLP-based intelligent search solutions with just a few lines of codeCreate both real-time and batch document processing pipelines using PythonWho this book is for If you're an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful. |
| call center sentiment analysis: Design, Operation and Evaluation of Mobile Communications Gavriel Salvendy, June Wei, 2021-07-03 This conference proceeding LNCS 12796 constitutes the thoroughly refereed proceedings of the 2nd International Conference on Design, Operation and Evaluation of Mobile Communications, MOBILE 2021 which was held as part of the 23rd HCI International Conference, HCII 2021 as a virtual event, due to COVID-19, in July 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes were carefully reviewed and selected from 5222 submissions. MOBILE 2021 includes a total of 27 papers; they were organized in topical sections named: Designing, Developing and Evaluating Mobile Interaction Systems and User Experience, Acceptance and Impact of Mobile Communications. |
| call center sentiment analysis: Social Data Analytics Krish Krishnan, Shawn P. Rogers, 2014-11-10 Social Data Analytics is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease. You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization's next social data analytics project. - Provides foundational understanding of new and emerging technologies—social data, collaboration, big data, advanced analytics - Includes case studies and practical examples of success and failures - Will prepare you to lead projects and advance initiatives that will benefit you and your organization |
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