Low Resource Social Media Text Mining

Low Resource Social Media Text Mining (SpringerBriefs in Computer Science)
3 Oct. 2021
Author: Shriphani Palakodety,Ashiqur R. KhudaBukhsh ,Guha Jayachandran (Author)

Publisher Finelybook 出版社:Springer; 1st ed. 2021 edition (3 Oct. 2021)
Language 语言:English
pages 页数:71 pages
ISBN-10 书号:981165624X
ISBN-13 书号:9789811656248

Book Description
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 Author: a Western user base. Such content is authored in English Author: a user base fluent in the language and can be processed Author: 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 language skill 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.

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