Extracting Knowledge From Opinion MiningКНИГИ » ОС И БД
Название: Extracting Knowledge From Opinion Mining Автор: Rashmi Agrawal, Neha Gupta Издательство: IGI Global Серия: Advances in Data Mining and Database Management Год: 2018 Страниц: 375 Язык: английский Формат: pdf (true) Размер: 13.1 MB
Data mining techniques are commonly used to extract meaningful information from the web, such as data from web documents, website usage logs, and hyperlinks. Building on this, modern organizations are focusing on running and improving their business methods and returns by using opinion mining.
In this book, through introducing the Deep Learning and relation between Deep Learning (DL) and Artificial Intelligence (AI), and especially Machine Learning (ML), the authors discuss machine learning and deep learning techniques, the literature focuses on applied deep learning techniques for extracting opinions. It can be found that opinion mining without using deep learning is not meaningful. In this way, authors mention the history of deep learning and appearance of it and some important and useful deep learning algorithms for opinion mining; learning methods and customized deep learning techniques for opinion mining will also be described to understand how these algorithms and techniques are used as an applicable solution. Future trends of deep learning in opinion mining are introduced through some clues about the applications and future usages of deep learning and opinion mining and how intelligent agents develop automatic deep learning.
Extracting Knowledge From Opinion Mining is an essential resource that presents detailed information on web mining, business intelligence through opinion mining, and how to effectively use knowledge retrieved through mining operations. While highlighting relevant topics, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for information technology professionals within research or business settings, graduate and post-graduate students, as well as scholars.
Foreword.................... xvi Preface......................xviii Acknowledgment.............. xxvi Section 1 Introductory Concepts of Opinion Mining Chapter 1 Fundamentals of Opinion Mining.................1 Chapter 2 Feature Based Opinion Mining...................20 Chapter 3 Deep Learning for Opinion Mining...............40 Chapter 4 Opinion Mining: Using Machine Learning Techniques..........66 Section 2 Ontologies and Their Applications Chapter 5 Ontology-Based Opinion Mining..........84 Chapter 6 Ontologies, Repository, and Information Mining in Component-Based Software Engineering Environment........104 Chapter 7 Ontology-Based Opinion Mining for Online Product Reviews...........123 Chapter 8 Applications of Ontology-Based Opinion Mining...........149 Section 3 Tools and Techniques of Opinion Mining Chapter 9 Tools of Opinion Mining......................179 Chapter 10 Sentimental Analysis Tools..................204 Chapter 11 Anatomizing Lexicon With Natural Language Tokenizer Toolkit 3...........232 Section 4 Challenges and Open Issues of Opinion Mining Chapter 12 Challenges of Text Analytics in Opinion Mining.............268 Chapter 13 Open Issues in Opinion Mining.......................283 Section 5 Case Study Chapter 14 Case Study: Efficient Faculty Recruitment Using Genetic Algorithm...............299 Compilation of References....................... 312 About the Contributors......................... 337 Index........................ 344
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Extracting Knowledge From Opinion Mining 
