Overview
- Introduces the various challenges associated with social media content and quantifies these issues
- Features methods that are unsupervised or require minimal supervision
- Is designed for NLP practitioners well versed in recent advances in the field
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (6 chapters)
Keywords
About this book
This book focuses on methods that are unsupervised or require minimal supervision—vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied languageskill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text.
This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment.
On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.Authors and Affiliations
About the authors
Ashiqur Khuda Bukhsh is a project scientist at Carnegie Mellon University. He received his PhD in Computer Science from CMU.
Guha Jayachandran is the CEO and founder of Onai, USA. He received Ph.D. in Computer Science from Stanford University.
Bibliographic Information
Book Title: Low Resource Social Media Text Mining
Authors: Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Guha Jayachandran
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-981-16-5625-5
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Softcover ISBN: 978-981-16-5624-8Published: 03 October 2021
eBook ISBN: 978-981-16-5625-5Published: 01 October 2021
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XI, 60
Number of Illustrations: 6 b/w illustrations, 8 illustrations in colour
Topics: Artificial Intelligence, Machine Learning