A Practical Guide to Sentiment Analysis
Published on 7. April 2017
VII, 196 pages
978-3-319-55394-8 (ISBN)
Description
Sentiment analysis research has been started long back and recently it is one of the demanding research topics. Research activities on Sentiment Analysis in natural language texts and other media are gaining ground with full swing. But, till date, no concise set of factors has been yet defined that really affects how writers' sentiment i.e., broadly human sentiment is expressed, perceived, recognized, processed, and interpreted in natural languages. The existing reported solutions or the available systems are still far from perfect or fail to meet the satisfaction level of the end users. The reasons may be that there are dozens of conceptual rules that govern sentiment and even there are possibly unlimited clues that can convey these concepts from realization to practical implementation. Therefore, the main aim of this book is to provide a feasible research platform to our ambitious researchers towards developing the practical solutions that will be indeed beneficial for our society,business and future researches as well.
More details
Series
Edition
1st ed. 2017
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
VII, 196 p. 16 illus., 7 illus. in color.
File size
2,66 MB
ISBN-13
978-3-319-55394-8 (9783319553948)
DOI
10.1007/978-3-319-55394-8
Schweitzer Classification
Other editions
Additional editions
Erik Cambria | Dipankar Das | Sivaji Bandyopadhyay
A Practical Guide to Sentiment Analysis
Book
04/2017
Springer
€160.49
Shipment within 10-15 days
Content
Preface.- Affective Computing and Sentiment Analysis.- Many Facets of Sentiment Analysis .- Reflections on Sentiment/Opinion Analysis .- Challenges in Sentiment Analysis.- Sentiment Resources: Lexicons and Datasets.- Generative Models for Sentiment Analysis and Opinion Mining.- Social Media Summarization.- Deception Detection and Opinion Spam .- Concept-Level Sentiment Analysis with SenticNet.- Index.
