Sentiment Analysis and Ontology Engineering: An Environment of Computational Intelligence, Pedrycz Witold, Chen Shyi-Ming
Автор: Witold Pedrycz; Shyi-Ming Chen Название: Sentiment Analysis and Ontology Engineering ISBN: 3319303171 ISBN-13(EAN): 9783319303178 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Fundamentals of Sentiment Analysis and Its Applications.- Fundamentals of Sentiment Analysis: Concepts and Methodology.- The Comprehension of Figurative Language: What is the Influence of Irony and Sarcasm on NLP Techniques?.- Probabilistic Approaches for Sentiment Analysis: Latent Dirichlet Allocation for Ontology Building and Sentiment Extraction.- Description Logic Class Expression Learning Applied to Sentiment Analysis.- Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation.- Hyperelastic-based Adaptive Dynamics Methodology in Knowledge Acquisition for Computational Intelligence on Ontology Engineering of Evolving Folksonomy Driven Environment.- Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework.- Interpretability of Computational Models for Sentiment Analysis.- Chinese Micro-blog Emotion Classification by Exploiting Linguistic Features and SVMperf.- Social Media and News Sentiment Analysis for Advanced Investment Strategies.- Context Aware Customer Experience Management: A Development Framework Based on Ontologies and Computational Intelligence.- An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief.- Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing.- Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction.- OntoLSA: An Integrated Text Mining System for Ontology Learning and Sentiment Analysis.- Knowledge-based Tweet Classification for Disease Sentiment Monitoring.
Автор: Ranjan Satapathy; Erik Cambria; Amir Hussain Название: Sentiment Analysis in the Bio-Medical Domain ISBN: 3319886096 ISBN-13(EAN): 9783319886091 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Поставка под заказ.
Автор: Liu, Bing (university Of Illinois, Chicago) Название: Sentiment analysis ISBN: 1108486371 ISBN-13(EAN): 9781108486378 Издательство: Cambridge Academ Рейтинг: Цена: 10611.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sentiment analysis is the computational study of people`s opinions, emotions, and attitudes. This comprehensive introduction covers all core areas useful for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. The second edition includes new deep learning analysis methods.
Описание: This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis.
Описание: Part I. Fundamentals.- Large-Scale Clustering Algorithms.- On High Dimensional Search Space and Learning Methods.-Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification.- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders.- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing.- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data.- An Efficient Approach for Mining High Utility Itemsets over Data Streams.- Event Detection in Location-Based Social Networks.- Part II. Applications.- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey.- Big Data for Effective Management of Smart Grids.- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics.- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science.- Index.
Описание: Part I. Fundamentals.- Large-Scale Clustering Algorithms.- On High Dimensional Search Space and Learning Methods.-Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification.- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders.- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing.- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data.- An Efficient Approach for Mining High Utility Itemsets over Data Streams.- Event Detection in Location-Based Social Networks.- Part II. Applications.- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey.- Big Data for Effective Management of Smart Grids.- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics.- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science.- Index.
Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: Adam E Gaw?da; Janusz Kacprzyk; Leszek Rutkowski; Название: Advances in Data Analysis with Computational Intelligence Methods ISBN: 3319885162 ISBN-13(EAN): 9783319885162 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание: This book is a tribute to Professor Jacek ?urada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek ?urada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few.At the same time, the book recognizes and honors Professor ?urada’s dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world’s largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields.The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
Автор: Christian Moewes; Andreas N?rnberger Название: Computational Intelligence in Intelligent Data Analysis ISBN: 3642430856 ISBN-13(EAN): 9783642430855 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Complex systems and their phenomena are ubiquitous as they can befound in biology, finance, the humanities, management sciences,medicine, physics and similar fields.For many problems in these fields, there are no conventional ways tomathematically or analytically solve them completely at low cost.
Автор: D.P. Acharjya; Satchidananda Dehuri; Sugata Sanyal Название: Computational Intelligence for Big Data Analysis ISBN: 3319362003 ISBN-13(EAN): 9783319362007 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing.
Описание: Experimental data analysis is at the core of scientific inquiry, and computers have taken this function to a new level. This volume is an interactive guide to complex modern analytical processes from non-linear curve fitting to clustering and machine learning.
Автор: Slawomir Wierzcho?; Mieczyslaw K?opotek Название: Modern Algorithms of Cluster Analysis ISBN: 3319887521 ISBN-13(EAN): 9783319887524 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
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