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## Meet the editor

Alberto Cano is an Assistant Professor with the Department of Computer Science, Virginia Commonwealth University, USA, where he heads the High-Performance Data Mining Lab. He obtained his PhD degree in Computer Science from the University of Granada, Spain, in 2014. His research is focused on machine learning, data mining, general-purpose computing on graphics processing units, Apache Spark, and evolutionary computation.

He has published over 45 articles in high-impact factor journals, made 50 contributions to conferences, two book chapters, and one book. His research is supported by an Amazon AWS Machine Learning award and the VCU Presidential Research Quest Fund. Dr Cano is an IEEE Senior Member, ACM Member, and Associate Editor of IEEE Access and Applied Intelligence.

Contents

*by Alberto Cano*

*by Keiko Tsujioka*

**Preface III**

**Chapter 1 1**

**Chapter 2 9**

**Chapter 3 25**

**Chapter 4 41**

**Chapter 5 63**

Introductory Chapter: Data Streams and Online Learning in Social Media

Automatic Speech Emotion Recognition Using Machine Learning *by Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof,* 

A Case Study of Using Big Data Processing in Education: Method of Matching Members by Optimizing Collaborative Learning Environment

Information and Communication- Based Collaborative Learning and

*Mohamed Ali Mahjoub and Catherine Cleder*

Literature Review on Big Data Analytics Methods *by Iman Raeesi Vanani and Setareh Majidian*

*by Nityashree Nadar and R. Kamatchi*

Behavior Modeling Using Machine Learning Algorithm

## Contents


Preface

Social media has transformed society and the way people interact with each other. The volume and speed in which new structured and unstructured content is being generated surpasses the processing capacity of traditional machine learning and data mining systems. Analyzing such data demands new approaches coming from natural language processing, text mining, sentiment analysis, big data computing, and deep learning to understand the content and resolve the arising challenges. Identification of spam, fake news, hate speech, communities, influence analysis, threats, etc. in the ever-increasing networks are among the top hot topics of

machine learning and artificial intelligence in social media analytics. There is a need to develop robust, adaptable, and evolvable systems to tackle these open issues in real time in the context of the big data era and the Internet of the things, as well as to provide a meaningful and comprehensible summarization and visualization to the end users. This book provides the reader with a comprehensive overview of the latest developments in social media and machine learning, addressing research

innovations, applications, trends, and open challenges in this crucial area.

munication, and behavior modeling using machine learning algorithms.

**Alberto Cano**

Richmond, VA, USA

Department of Computer Science, Virginia Commonwealth University,

Chapter 1 presents an introduction to online machine learning and data stream mining in social media. Chapter 2 presents a system for automatic speech emotion recognition using machine learning. Chapter 3 presents a case study using big data processing in education and introduces a method of matching members by optimizing collaborative learning environments. Chapter 4 presents a literature review on big data analytics that covers the extensive work in the area for the last decade. Chapter 5 presents a study on collaborative learning based on information and com-
