Data Mining Approaches for Big Data and Sentiment Analysis in Social Media

by Ahmed A. Abd El-Latif, Brij B. Gupta, Dragan Perakovic

Length: 336 pages
Edition: 1
Language: English
Publisher: Engineering Science Reference
Publication Date: 2021-11-17
ISBN-10: 1799884147
ISBN-13: 9781799884149
Sales Rank: #0 (See Top 100 Books)

Social media sites are constantly evolving with huge amounts of scattered data or big data, which makes it difficult for researchers to trace the information flow. It is a daunting task to extract a useful piece of information from the vast unstructured big data; the disorganized structure of social media contains data in various forms such as text and videos as well as huge real-time data on which traditional analytical methods like statistical approaches fail miserably. Due to this, there is a need for efficient data mining techniques that can overcome the shortcomings of the traditional approaches. Data Mining Approaches for Big Data and Sentiment Analysis in Social Media encourages researchers to explore the key concepts of data mining, such as how they can be utilized on online social media platforms, and provides advances on data mining for big data and sentiment analysis in online social media, as well as future research directions. Covering a range of concepts from machine learning methods to data mining for big data analytics, this book is ideal for graduate students, academicians, faculty members, scientists, researchers, data analysts, social media analysts, managers, and software developers who are seeking to learn and carry out research in the area of data mining for big data and sentiment.

Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Dedication
EDITORIAL ADVISORY BOARD
Preface
Acknowledgment
Chapter 1: Approaches and Applications for Sentiment Analysis
ABSTRACT
INTRODUCTION
BACKGROUND
FOCUS OF THE ARTICLE
LEVELS OF ANALYSIS
SENTIMENT ANALYSIS APPROACHES AND TECHNIQUES
LEXICON-BASED AND NATURAL LANGUAGE PROCESSING TECHNIQUES
OTHER TECHNIQUES
INTERPRETING THE RESULTS
CONCLUSION AND FUTURE RESEARCH DIRECTIONS
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 2: A Survey on Building Recommendation Systems Using Data Mining Techniques
ABSTRACT
INTRODUCTION
MAIN OBJECTIVE
LITERATURE REVIEW
CATEGORIZATIONS OF HYBRIDIZATION RECOMMENDATION
ALGORITHM IMPLEMENTATION FOR HYBRID RECOMMENDER SYSTEM
CONCLUSION
REFERENCES
Chapter 3: A Survey on Sentiment Analysis Techniques for Twitter
ABSTRACT
1. INTRODUCTION
2. TWITTER OVERVIEW AND RELATED RESEARCH
3. EVALUATION METRICS FOR SENTIMENT ANALYSIS
4. STEPS OF SENTIMENT ANALYSIS
5. LEVELS OF SENTIMENT ANALYSIS
6. APPROACHES FOR SENTIMENT ANALYSIS
7. TWITTER SENTIMENT ANALYSIS TOOLS
8. STANDARD DATASETS AVAILABLE FOR TWITTER SENTIMENT ANALYSIS
9. SIGNIFICANCE OF SENTIMENT ANALYSIS
10. LIMITATION OF SENTIMENT ANALYSIS
11. OPEN ISSUES AND CHALLENGES
12. CONCLUSION
REFERENCES
Chapter 4: Role of Social Media in the COVID-19 Pandemic
ABSTRACT
INTRODUCTION
DIGITALIZATION IN COVID-19 PANDEMIC
USE OF SOCIAL MEDIA FOR VARIOUS PURPOSES
CONCLUSION
REFERENCES
Chapter 5: Data Mining Approaches for Sentiment Analysis in Online Social Networks (OSNs)
ABSTRACT
INTRODUCTION
METHODOLOGY
RELATED WORKS
MINING OF THE SOCIAL BIG DATA: FUNDAMENTAL PRINCIPLES
METHODS
TRENDS IN OPINION MINING AND SENTIMENT RESEARCH
CONCLUSION AND FUTURE RESEARCH DIRECTIONS
REFERENCES
Chapter 6: Sentiment Analysis and Summarization of Facebook Posts on News Media
ABSTRACT
1. INTRODUCTION
2. EXISTING APPLICATION
3. DESIGN OF OUR APPLICATION
4. EVALUATION
5. CONCLUSION AND FUTURE WORK
REFERENCES
ENDNOTES
Chapter 7: An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network
ABSTRACT
1. INTRODUCTION
2. METHODOLOGY
3. PERFORMANCE EVALUATION AND ANALYSIS
4. DISCUSSION OF FUTURE RESEARCH DIRECTIONS
5. CONCLUSION
REFERENCES
Chapter 8: Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. SYSTEM MODEL AND METHODS
4. EXPERIMENTAL RESULTS
5. CONCLUSION
REFERENCES
APPENDIX
Chapter 9: The Stakes of Social Media
ABSTRACT
INTRODUCTION
BACKGROUND
MAIN FOCUS OF THE CHAPTER
SOLUTIONS AND RECOMMENDATIONS
FUTURE RESEARCH DIRECTIONS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
ADDITIONAL READING
KEY TERMS AND DEFINITIONS
Chapter 10: Predicting Catastrophic Events Using Machine Learning Models for Natural Language Processing
ABSTRACT
INTRODUCTION
LITERARY WORK & MOTIVATION
METHODOLOGIES
MODELS
CONCLUSION
REFERENCES
Chapter 11: Clubhouse Experience
ABSTRACT
INTRODUCTION
BACKGROUND
SENTIMENT ANALYSIS OF CLUBHOUSE DATA
CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
ENDNOTE
Compilation of References
About the Contributors