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:
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