| call center dataset for 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 dataset for sentiment analysis: Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska, 2023-10-25 Today, communication technology and computer engineering are intertwined, with advances in one field driving advances in the other, leading to the development of outstanding technologies. This book delves into the latest trends and breakthroughs in the areas of communication, Internet of things, cloud computing, big data, artificial intelligence, and machine learning. This book discusses challenges and opportunities that arise with the integration of communication technology and computer engineering. In addition, the book examines the ethical and social implications, including issues related to privacy, security, and digital divide and law. We have explored the future direction of these fields and the potential for further breakthroughs and innovations. The book is intended for a broad audience of undergraduate and graduate students, practicing engineers, and readers without a technical background who have an interest in learning about communication technology and computer engineering. |
| call center dataset for 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 dataset for sentiment analysis: Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications D. Jude Hemanth, 2024-01-19 Sentiment Analysis has become increasingly important in recent years for nearly all online applications. Sentiment Analysis depends heavily on Artificial Intelligence (AI) technology wherein computational intelligence approaches aid in deriving the opinions/emotions of human beings. With the vast increase in Big Data, computational intelligence approaches have become a necessity for Natural Language Processing and Sentiment Analysis in a wide range of decision-making application areas. The applications of Sentiment Analysis are enormous, ranging from business to biomedical and clinical applications. However, the combination of AI methods and Sentiment Analysis is one of the rarest commodities in the literature. The literatures either gives more importance to the application alone or to the AI/CI methodology.Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical coverage of AI-based Sentiment Analysis methods for various applications. The authors provide readers with an in-depth look at the challenges and solutions associated with the different types of Sentiment Analysis, including case studies and real-world scenarios from across the globe. Development of scientific and enterprise applications are covered, which will aid computer scientists in building practical/real-world AI-based Sentiment Analysis systems. - Includes basic concepts, technical explanations, and case studies for in-depth explanation of the Sentiment Analysis - Aids computer scientists in developing practical/real-world AI-based Sentiment Analysis systems - Provides readers with real-world development applications of AI-based Sentiment Analysis, including transfer learning for opinion mining from pandemic medical data, sarcasm detection using neural networks in human-computer interaction, and emotion detection using the random-forest algorithm |
| call center dataset for 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 dataset for sentiment analysis: Artificial Intelligence Jude Hemanth, Thushari Silva, Asoka Karunananda, 2019-07-04 This book constitutes the refereed proceedings of the Second International Conference, SLAAI-ICAI 2018, held in Moratuwa, Sri Lanka, in December 2018. The 32 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in the following topical sections: intelligence systems; neural networks; game theory; ontology engineering; natural language processing; agent based system; signal and image processing. |
| call center dataset for sentiment analysis: Social Media, Sociality, and Survey Research Craig A. Hill, Elizabeth Dean, Joe Murphy, 2013-09-25 Provides the knowledge and tools needed for the future of survey research The survey research discipline faces unprecedented challenges, such as falling response rates, inadequate sampling frames, and antiquated approaches and tools. Addressing this changing landscape, Social Media, Sociality, and Survey Research introduces readers to a multitude of new techniques in data collection in one of the fastest developing areas of survey research. The book is organized around the central idea of a sociality hierarchy in social media interactions, comprised of three levels: broadcast, conversational, and community based. Social Media, Sociality, and Survey Research offers balanced coverage of the theory and practice of traditional survey research, while providing a conceptual framework for the opportunities social media platforms allow. Demonstrating varying perspectives and approaches to working with social media, the book features: New ways to approach data collection using platforms such as Facebook and Twitter Alternate methods for reaching out to interview subjects Design features that encourage participation with engaging, interactive surveys Social Media, Sociality, and Survey Research is an important resource for survey researchers, market researchers, and practitioners who collect and analyze data in order to identify trends and draw reliable conclusions in the areas of business, sociology, psychology, and population studies. The book is also a useful text for upper-undergraduate and graduate-level courses on survey methodology and market research. |
| call center dataset for sentiment analysis: Data Science and Big Data Analytics EMC Education Services, 2015-01-05 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| call center dataset for 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. |
| call center dataset for 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 dataset for sentiment analysis: New Challenges in Distributed Information Filtering and Retrieval Cristian Lai, Giovanni Semeraro, Eloisa Vargiu, 2012-08-10 This volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the DART 2011 Workshop, held in Palermo (Italy), on September 2011, and co-located with the XII International Conference of the Italian Association on Artificial Intelligence. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as Multi-Agent Systems, Natural Language Processing, Automatic Advertisement, Customer Interaction Analytics, Opinion Mining. |
| call center dataset for sentiment analysis: Pythonic AI Arindam Banerjee, 2023-10-31 Unlock the power of AI with Python: Your Journey from Novice to Neural Nets KEY FEATURES ● Learn to code in Python and use Google Colab's hardware accelerators (GPU and TPU) to train and deploy AI models efficiently. ● Develop Convolutional Neural Networks (CNNs) using the TensorFlow 2 library for computer vision tasks. ● Develop sequence, attention-based, and Transformer models using the TensorFlow 2 library for Natural Language Processing (NLP) tasks. DESCRIPTION “Pythonic AI” is a book that teaches you how to build AI models using Python. It also includes practical projects in different domains so you can see how AI is used in the real world. Besides teaching how to build AI models, the book also teaches how to understand and explore the opportunities that AI presents. It includes several hands-on projects that walk you through successful AI applications, explaining concepts like neural networks, computer vision, natural language processing (NLP), and generative models. Each project in the book also reiterates and reinforces the important aspects of Python scripting. You'll learn Python coding and how it can be used to build cutting-edge AI applications. The author explains each essential line of Python code in detail, taking into account the importance and difficulty of understanding. By the end of the book, you will learn how to develop a portfolio of AI projects that will help you land your dream job in AI. WHAT YOU WILL LEARN ● Create neural network models using the TensorFlow 2 library. ● Develop Convolutional Neural Networks (CNNs) for computer vision tasks. ● Develop Sequence models for Natural Language Processing (NLP) tasks. ● Create Attention-based and Transformer models. ● Learn how to create Generative Adversarial Networks (GANs). WHO THIS BOOK IS FOR This book is for everyone who wants to learn how to build AI applications in Python, regardless of their experience level. Whether you're a student, a tech professional, a non-techie, or a technology enthusiast, this book will teach you the fundamentals of Python and AI, and show you how to apply them to real-world problems. TABLE OF CONTENTS 1. Python Kickstart: Concepts, Libraries, and Coding 2. Setting up AI Lab 3. Design My First Neural Network Model 4. Explore Designing CNN with TensorFlow 5. Develop CNN-based Image Classifier Apps 6. Train and Deploy Object Detection Models 7. Create a Text and Image Reader 8. Explore NLP for Advanced Text Analysis 9. Up and Running with Sequence Models 10. Using Sequence Models for Automated Text Classification 11. Create Attention and Transformer Models 12. Generating Captions for Images 13. Learn to Build GAN Models 14. Generate Artificial Faces Using GAN |
| call center dataset for sentiment analysis: Modern Computational Models of Semantic Discovery in Natural Language Žižka, Jan, 2015-07-17 Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities? Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery. This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others. |
| call center dataset for sentiment analysis: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2016-12-12 Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence. |
| call center dataset for sentiment analysis: Artificial Intelligence and Soft Computing Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada, 2019-05-27 The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019. The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation. The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control. |
| call center dataset for sentiment analysis: Natural Language Processing with SAS , 2020-08-31 Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and emulate written or spoken human language. NLP draws from many disciplines including human-generated linguistic rules, machine learning, and deep learning to fill the gap between human communication and machine understanding. The papers included in this special collection demonstrate how NLP can be used to scale the human act of reading, organizing, and quantifying text data. |
| call center dataset for 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 dataset for sentiment analysis: Data-Centric Artificial Intelligence for Multidisciplinary Applications Parikshit N Mahalle, Namrata Nishant Wasatkar, Gitanjali R. Shinde, 2024-06-06 This book explores the need for a data‐centric AI approach and its application in the multidisciplinary domain, compared to a model‐centric approach. It examines the methodologies for data‐centric approaches, the use of data‐centric approaches in different domains, the need for edge AI and how it differs from cloud‐based AI. It discusses the new category of AI technology, data‐centric AI (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‐centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods. • Includes a collection of case studies with experimentation results to adhere to the practical approaches • Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways • Discusses methodologies to achieve accurate results by improving the quality of data • Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications |
| call center dataset for sentiment analysis: AI Technologies for Information Systems and Management Science Lalit Garg, |
| call center dataset for 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. |
| call center dataset for 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 dataset for sentiment analysis: Large Language Models Oswald Campesato, 2024-09-17 This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, Meta AI, Claude 3, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. FEATURES: Covers in-depth explanations of foundational and advanced LLM concepts, including BERT, GPT-4, and prompt engineering Uses practical Python code samples in leveraging LLM functionalities effectively Discusses future trends, ethical considerations, and the evolving landscape of AI technologies Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase) |
| call center dataset for sentiment analysis: Practical AI for Cybersecurity Ravi Das, 2021-02-26 The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly. |
| call center dataset for 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. |
| call center dataset for 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 dataset for sentiment analysis: Intelligent Systems Design and Applications Ajith Abraham, Sabri Pllana, Gabriella Casalino, Kun Ma, Anu Bajaj, 2023-07-04 This book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering. |
| call center dataset for sentiment analysis: Recent Developments and the New Direction in Soft-Computing Foundations and Applications Lotfi A. Zadeh, Ronald R. Yager, Shahnaz N. Shahbazova, Marek Z. Reformat, Vladik Kreinovich, 2018-05-28 This book is an authoritative collection of contributions in the field of soft-computing. Based on selected works presented at the 6th World Conference on Soft Computing, held on May 22-25, 2016, in Berkeley, USA, it describes new theoretical advances, as well as cutting-edge methods and applications. Theories cover a wealth of topics, such as fuzzy logic, cognitive modeling, Bayesian and probabilistic methods, multi-criteria decision making, utility theory, approximate reasoning, human-centric computing and many others. Applications concerns a number of fields, such as internet and semantic web, social networks and trust, control and robotics, computer vision, medicine and bioinformatics, as well as finance, security and e-Commerce, among others. Dedicated to the 50th Anniversary of Fuzzy Logic and to the 95th Birthday Anniversary of Lotfi A. Zadeh, the book not only offers a timely view on the field, yet it also discusses thought-provoking developments and challenges, thus fostering new research directions in the diverse areas of soft computing. |
| call center dataset for 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. |
| call center dataset for sentiment analysis: Natural Language Processing and Chinese Computing Fei Liu, Nan Duan, Qingting Xu, Yu Hong, 2023-10-07 This three-volume set constitutes the refereed proceedings of the 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, held in Foshan, China, during October 12–15, 2023. The ____ regular papers included in these proceedings were carefully reviewed and selected from 478 submissions. They were organized in topical sections as follows: dialogue systems; fundamentals of NLP; information extraction and knowledge graph; machine learning for NLP; machine translation and multilinguality; multimodality and explainability; NLP applications and text mining; question answering; large language models; summarization and generation; student workshop; and evaluation workshop. |
| call center dataset for sentiment analysis: Introduction to Text Visualization Nan Cao, Weiwei Cui, 2016-10-22 This book provides a systematic review of many advanced techniques to support the analysis of large collections of documents, ranging from the elementary to the profound, covering all the aspects of the visualization of text documents. Particularly, we start by introducing the fundamental concept of information visualization and visual analysis, followed by a brief survey of the field of text visualization and commonly used data models for converting document into a structured form for visualization. Then we introduce the key visualization techniques including visualizing document similarity, content, sentiments, as well as text corpus exploration system in details with concrete examples in the rest of the book. |
| call center dataset for sentiment analysis: Progresses in Artificial Intelligence and Neural Systems Anna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, Eros Pasero, 2020-07-09 This book provides an overview of the current advances in artificial intelligence and neural nets. Artificial intelligence (AI) methods have shown great capabilities in modelling, prediction and recognition tasks supporting human–machine interaction. At the same time, the issue of emotion has gained increasing attention due to its relevance in achieving human-like interaction with machines. The real challenge is taking advantage of the emotional characterization of humans’ interactions to make computers interfacing with them emotionally and socially credible. The book assesses how and to what extent current sophisticated computational intelligence tools might support the multidisciplinary research on the characterization of appropriate system reactions to human emotions and expressions in interactive scenarios. Discussing the latest recent research trends, innovative approaches and future challenges in AI from interdisciplinary perspectives, it is a valuable resource for researchers and practitioners in academia and industry. |
| call center dataset for sentiment analysis: Text Analytics for Business Decisions Andres Fortino, 2021-05-13 With the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today’s most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented later Uses Excel and R for datasets in case studies and exercises Features the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data mining Companion files with numerous datasets and figures from the text. The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com. |
| call center dataset for sentiment analysis: Data Analytics with Artificial Intelligence: Transforming Big Data into Valuable Information Mehmet Ali Yilbasi, 2023-06-11 This ebook is a guide for anyone who wants to understand the impact of Data Analytics and Artificial Intelligence in business and explore how these technologies can be applied. Businesses should use this association correctly to extract more valuable information from large data sets, optimize their operational processes and gain competitive advantage. Throughout our book, we will try to explain the potential in Data Analytics and Artificial Intelligence with examples, practical tips and real-world applications. We will also provide resources and recommendations for our readers who want to follow developments in these areas. |
| call center dataset for sentiment analysis: Machine Intelligence Suresh Samudrala, 2019-01-11 Artificial intelligence and machine learning are considered as hot technologies of this century. As these technologies move from research labs to enterprise data centers, the need for skilled professionals is continuously on the rise. This book is intended for IT and business professionals looking to gain proficiency in these technologies but are turned off by the complex mathematical equations. This book is also useful for students in the area of artificial intelligence and machine learning to gain a conceptual understanding of the algorithms and get an industry perspective. This book is an ideal place to start your journey as • Core concepts of machine learning algorithms are explained in plain English using illustrations, data tables and examples • Intuitive meaning of the mathematics behind popular machine learning algorithms explained • Covers classical machine learning, neural networks and deep learning algorithms At a time when the IT industry is focusing on reskilling its vast human resources, Machine intelligence is a very timely publication. It has a simple approach that builds up from basics, which would help software engineers and students looking to learn about the field as well as those who might have started off without the benefit of a structured introduction or sound basics. Highly recommended. - Siddhartha S, Founder and CEO of Intain - Financial technology startup Suresh has written a very accessible book for practitioners. The book has depth yet avoids excessive mathematics. The coverage of the subject is very good and has most of the concepts required for understanding machine learning if someone is looking for depth. For senior management, it will provide a good overview. It is well written. I highly recommend it. - Whee Teck ONG, CEO of Trusted Source and VP of Singapore Computer Society |
| call center dataset for sentiment analysis: Microsoft Certified: Teams Administrator Associate (MS-700) , 2024-10-26 Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| call center dataset for 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. |
| call center dataset for sentiment analysis: Handbook of Artificial Intelligence Dumpala Shanthi, B. Madhuravani, Ashwani Kumar, 2023-11-13 Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector. This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with an introduction to AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry use-case demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more. Additional features of the book include a summary and references in every chapter, and several full-color images to visualize concepts for easy understanding. It is an ideal handbook for both students and instructors in undergraduate level courses in artificial intelligence, data science, engineering and computer science who are required to understand AI/ML in a practical context. |
| call center dataset for sentiment analysis: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics Teixeira, Sandrina, Remondes, Jorge, 2023-11-17 In today's rapidly evolving landscape, AI has become an indispensable tool for organizations seeking to enhance their understanding of customers, boost productivity, and foster stronger connections with their target audience. The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics is a comprehensive and timely exploration of the integration of artificial intelligence (AI) into the field of digital marketing. Authored by experts in the field, this book delves into the profound and far-reaching changes that AI is bringing to the digital marketing arena. It provides a detailed examination of how organizations can leverage AI technologies to gain a competitive edge in the market. By mastering these new technologies, companies can effectively navigate the dynamic digital landscape, optimize their marketing strategies, and deliver highly personalized content to their customers. Ideal for a wide range of audiences, including researchers, teachers, students, and executives, this book serves as a vital resource for those seeking to stay ahead of the curve in the ever-evolving world of digital marketing. Through its comprehensive coverage of AI applications in the field, it equips readers with the knowledge and insights necessary to make informed decisions, develop effective marketing strategies, and drive business growth. |
| call center dataset for sentiment analysis: Data Engineering and Intelligent Computing Vikrant Bhateja, Suresh Chandra Satapathy, Carlos M. Travieso-González, V. N. Manjunath Aradhya, 2021-05-04 This book features a collection of high-quality, peer-reviewed papers presented at the Fourth International Conference on Intelligent Computing and Communication (ICICC 2020) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 18–20 September 2020. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care. |
| call center dataset for sentiment analysis: Predictive Computing and Information Security P.K. Gupta, Vipin Tyagi, S.K. Singh, 2017-09-27 This book describes various methods and recent advances in predictive computing and information security. It highlights various predictive application scenarios to discuss these breakthroughs in real-world settings. Further, it addresses state-of-art techniques and the design, development and innovative use of technologies for enhancing predictive computing and information security. Coverage also includes the frameworks for eTransportation and eHealth, security techniques, and algorithms for predictive computing and information security based on Internet-of-Things and Cloud computing. As such, the book offers a valuable resource for graduate students and researchers interested in exploring predictive modeling techniques and architectures to solve information security, privacy and protection issues in future communication. |
Call Center Dataset For Sentiment Analysis Full PDF
Call Center Dataset For Sentiment Analysis: Acoustic Feature-based Sentiment Analysis of Call Center Data Zeshan Peng (Graduate of University of Missouri--Columbia),2017 With the …
Acoustic Feature-Based Sentiment Analysis of Call Center Data
determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive …
Call Center Dataset For Sentiment Analysis
sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic …
ACOUSTIC AND LEXICAL SENTIMENT ANALYSIS FOR …
In this paper we explore architectures for developing a senti-ment recognizer on real-world call center data using acoustic and lexical cues. Such a scenario involves a series of challenges …
Sentiment Analysis of Call Centre Audio Conversations using …
In particular in this work, we contribute to the field by proposing a novel model that combines speech recognition technologies to analyze sentiment from calls using text classification …
AI-Powered Sentiment Analysis for Call Centers - Axxess …
AI-based sentiment analysis uses a dynamic database to ensure your agents are aware of their caller’s sentiments before they even pick up the phone or answer the chat. When tuned into …
Using AI technology to optimize call center outcomes - NetApp
Sentiment analysis uses natural language processing (NLP) to determine whether the sentiment expressed during a customer call is positive, negative, or neutral. Using this approach, your …
PREDICTING USTOMER CALL NTENT Y ANALYZING …
To solve this problem, we develop a convolutional neural network (CNN)-based supervised learning model to classify the customer calls into four intent categories: sales, service, vendor …
Call Center Dataset For Sentiment Analysis(2) (2024)
CI methodology Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical …
REVIEW PAPER ON CALL CENTER SENTIMENT …
calls at the call center using aspect-based sentiment analysis on call center data. Classification lies at the heart of both human intelligence and machine intelligence.
Acoustic Feature-Based Sentiment Analysis of Call Center Data
good indicators for sentiment in our dataset • Temporal information can be captured by feeding feature matrixes into deep convolutional neural networks to improve
Sentiment Detection from Speech Recognition Output
In this paper we perform a review of established and novel features for text analysis, combine them with the latest deep learning algorithms and evaluate the proposed models for the needs …
Sentiment Analysis of Incoming Voice Calls - ijatem.com
This project aims to meet the increasing need for real-time sentiment analysis within voice call interactions, acknowledging the rising significance of voice-based engagements in today's …
Call Center Dataset For Sentiment Analysis (PDF)
Sentiment Analysis methods for various applications The authors provide readers with an in depth look at the challenges and solutions associated with the different types of Sentiment Analysis …
Centers Multi-Modal Sentiment Analysis Using Text and …
applications in call centers trained for either question-answering tasks or calculating sentiment out of user feedback via surveys. These existing expert systems only utilize text mining techniques …
Call Center Dataset For Sentiment Analysis
This thesis work focuses on determining sentiment from call center audio records each containing a conversation between a sales representative and a customer The sentiment of an audio …
Call Center Dataset For Sentiment Analysis - old.icapgen.org
Sentiment Analysis methods for various applications The authors provide readers with an in depth look at the challenges and solutions associated with the different types of Sentiment Analysis …
Call Center Dataset For Sentiment Analysis (2024)
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 …
Call Center Dataset For Sentiment Analysis (2024)
Sentiment Analysis systems Provides readers with real world development applications of AI based Sentiment Analysis including transfer learning for opinion mining from pandemic …
Call Center Dataset For Sentiment Analysis Full PDF
Call Center Dataset For Sentiment Analysis: Acoustic Feature-based Sentiment Analysis of Call Center Data Zeshan Peng (Graduate of University of Missouri--Columbia),2017 With the …
A Deep Learning System for Sentiment Analysis of Service …
In this paper, we target and use real-world data - service calls, which poses additional challenges with respect to the artificial datasets that have been typically used in the past in multimodal …
Acoustic Feature-Based Sentiment Analysis of Call Center Data
determining sentiment from call center audio records, each containing a conversation between a sales representative and a customer. The sentiment of an audio record is considered positive …
Call Center Dataset For Sentiment Analysis
sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic …
ACOUSTIC AND LEXICAL SENTIMENT ANALYSIS FOR …
In this paper we explore architectures for developing a senti-ment recognizer on real-world call center data using acoustic and lexical cues. Such a scenario involves a series of challenges …
Sentiment Analysis of Call Centre Audio Conversations using …
In particular in this work, we contribute to the field by proposing a novel model that combines speech recognition technologies to analyze sentiment from calls using text classification …
AI-Powered Sentiment Analysis for Call Centers - Axxess …
AI-based sentiment analysis uses a dynamic database to ensure your agents are aware of their caller’s sentiments before they even pick up the phone or answer the chat. When tuned into …
Using AI technology to optimize call center outcomes
Sentiment analysis uses natural language processing (NLP) to determine whether the sentiment expressed during a customer call is positive, negative, or neutral. Using this approach, your …
PREDICTING USTOMER CALL NTENT Y ANALYZING PHONE …
To solve this problem, we develop a convolutional neural network (CNN)-based supervised learning model to classify the customer calls into four intent categories: sales, service, vendor …
Call Center Dataset For Sentiment Analysis(2) (2024)
CI methodology Computational Intelligence for Sentiment Analysis in Natural Language Processing Applications provides a solution to this problem through detailed technical …
REVIEW PAPER ON CALL CENTER SENTIMENT ANALYSIS
calls at the call center using aspect-based sentiment analysis on call center data. Classification lies at the heart of both human intelligence and machine intelligence.
Acoustic Feature-Based Sentiment Analysis of Call Center Data
good indicators for sentiment in our dataset • Temporal information can be captured by feeding feature matrixes into deep convolutional neural networks to improve
Sentiment Detection from Speech Recognition Output
In this paper we perform a review of established and novel features for text analysis, combine them with the latest deep learning algorithms and evaluate the proposed models for the needs …
Sentiment Analysis of Incoming Voice Calls - ijatem.com
This project aims to meet the increasing need for real-time sentiment analysis within voice call interactions, acknowledging the rising significance of voice-based engagements in today's …
Call Center Dataset For Sentiment Analysis (PDF)
Sentiment Analysis methods for various applications The authors provide readers with an in depth look at the challenges and solutions associated with the different types of Sentiment Analysis …
Centers Multi-Modal Sentiment Analysis Using Text and …
applications in call centers trained for either question-answering tasks or calculating sentiment out of user feedback via surveys. These existing expert systems only utilize text mining techniques …
Call Center Dataset For Sentiment Analysis
This thesis work focuses on determining sentiment from call center audio records each containing a conversation between a sales representative and a customer The sentiment of an audio …
Call Center Dataset For Sentiment Analysis - old.icapgen.org
Sentiment Analysis methods for various applications The authors provide readers with an in depth look at the challenges and solutions associated with the different types of Sentiment Analysis …
Call Center Dataset For Sentiment Analysis (2024)
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 …
Call Center Dataset For Sentiment Analysis (2024)
Sentiment Analysis systems Provides readers with real world development applications of AI based Sentiment Analysis including transfer learning for opinion mining from pandemic …
