Low Resource Social Media Text Mining

This book discusses unsupervised or minimally supervised methods for processing user-generated text in social media, particularly in linguistically diverse regions like South Asia, where resources are scarce. It introduces challenges associated with multilingual content, highlights the need for new methods, and provides solutions and intuition using real-world social media data sets.

Format: Paperback / softback
Length: 60 pages
Publication date: 03 October 2021
Publisher: Springer Verlag, Singapore

This book delves into methods that operate with minimal or no supervision, which are crucial in the context of low-resource settings. Over the past few years, the rapid expansion of Internet access across the globe has led to a surge in user-generated text content on social media platforms. This phenomenon is particularly evident in linguistically diverse regions such as South Asia, where over 400 million people regularly engage with social media platforms. YouTube, Facebook, and Twitter collectively boast monthly active user bases exceeding 200 million users from this region.

In the realm of natural language processing (NLP), research and publicly available resources, including models and corpora, have primarily focused on web content authored by a Western user base. This content is typically written in English by users who are fluent in the language and can be processed using a wide range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions exhibits high levels of multilingualism, code-switching, and varying language skill levels. Resources such as corpora and models are scarce, further exacerbating the challenges associated with processing such text.

Given these constraints, there is a need for newer methods to handle the complexities of text mining in multilingual, low-resource environments. This book aims to address this gap by providing NLP practitioners with a comprehensive understanding of the challenges and solutions related to social media content in low-resource languages. The book begins by introducing the various challenges faced by social media content, including language diversity, data sparsity, and noise. It then quantifies these issues and presents solutions and intuitions to address them.

Whenever possible, the methods discussed in the book are evaluated on real-world social media data sets to demonstrate their robustness in the noisy social media environment. By the end of the book, readers will have a deep understanding of the complexities of text mining in multilingual, low-resource environments. They will be familiar with a broad range of off-the-shelf NLP tools and techniques that can be employed to tackle these challenges effectively.

In conclusion, this book is a valuable resource for NLP practitioners who are interested in expanding their expertise in low-resource multilingual NLP. It provides a comprehensive introduction to the challenges and solutions associated with social media content in low-resource languages, and offers practical insights and techniques that can be applied in real-world scenarios.

Weight: 131g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811656248
Edition number: 1st ed. 2021