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 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.
The many academic areas covered in this publication include, but are not limited to:
- Big Data Analytics
- Data Mining
- Machine Learning
- Market Analysis
- Multilingual Aspects of Sentiment Analysis
- Multimodal Sentiment Analysis
- Predictive Models
- Recommendation Systems
- Security of Social Media
- Sentiment Analysis
- Social Media
Book Release Date: December 2021