 Название: Sentiment Analysis and Knowledge Discovery in Contemporary Business
Автор: Dharmendra Singh Rajput, Ramjeevan Singh Thakur, S. Muzamil Basha
Издательство: IGI Global
ISBN: 1522549994
Год: 2019
Страниц: 356
Язык: английский
Формат: pdf (true)
Размер: 14.4 MB
In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through online collaborative media. However, conducting sentiment analysis on these platforms can be challenging, especially for business professionals who are using them to collect vital data.
In this age, the advancements in the arena of Machine learning are huge. Machine Learning has become one emerging field of computer science. As a part of many different algorithms that can learn form and make decisions on data are developed. Different algorithm works well in different situation. There are different machine learning application have been developed such as algorithms to filter e-mails, fraud detection, voice recognition, and weather prediction etc. There are different types of machine learning algorithms present in computer science: Linear Regression, Linear Discriminant Analysis, Support Vector Machine, Naive Bayes classifier, Neural Networks, & Decision trees. Thereafter each of these algorithms is explained with respect to its use, application and supported examples. Finally for each of the above stated algorithm a detailed line by line R Code explanation is provided.
Sentiment Analysis and Knowledge Discovery in Contemporary Business is an essential reference source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business environments. Featuring research on topics such as knowledge retrieval and knowledge updating, this book is ideally designed for business managers, academicians, business professionals, researchers, graduate-level students, and technology developers seeking current research on data collection and management to drive profit.
Contents:
Preface................ xv
Acknowledgment................ xxi
Section 1 Introduction and Applications of Sentiment Analysis
Chapter 1 A Study and Comparison of Sentiment Analysis Techniques Using Demonetization: Case Study........ 1
Chapter 2 Approaches to Sentiment Analysis on Product Reviews.................. 15
Chapter 3 Sentimental Analysis in Various Business Applications:............... 31
Chapter 4 A Survey on Implementation Methods and Applications of Sentiment Analysis.......... 44
Chapter 5 Analysis of Public Sentiments About Mega Online Sale Using Tweets on Big Billions Day Sale....... 59
Chapter 6 Application of Sentiment Analysis in Movie reviews................. 77
Section 2 Data Mining and Machine Learning Algorithms
Chapter 7 Implementation of Data Mining Algorithm With R............... 92
Chapter 8 Introduction and Implementation of Machine Learning Algorithms in R............. 126
Chapter 9 Conceptual Approach to Predict Loan Defaults Using Decision Trees............. 148
Chapter 10 Application of Data Mining Techniques in Weather Forecasting............... 162
Chapter 11 Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research.............. 175
Chapter 12 Data Visualization in R............... 205
Chapter 13 Ideating a Recommender System for Business Growth Using Profit Pattern Mining and Uncertainty Theory.............. 223
Section 3 Big Data Streams and Its Applications
Chapter 14 Mining Data Streams.................. 251
Chapter 15 Introduction to Big Data and Business Analytics................ 279
Chapter 16 Applications of Domain-Specific Predictive Analytics Applied to Big Data............... 289
Compilation of References.............. 307
About the Contributors................. 326
Index................. 332
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