Low Resource Social Media Text Mining

Low Resource Social Media Text Mining
Author :
Publisher : Springer Nature
Total Pages : 67
Release :
ISBN-10 : 9789811656255
ISBN-13 : 9811656258
Rating : 4/5 (55 Downloads)

Book Synopsis Low Resource Social Media Text Mining by : Shriphani Palakodety

Download or read book Low Resource Social Media Text Mining written by Shriphani Palakodety and published by Springer Nature. This book was released on 2021-10-01 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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.


Low Resource Social Media Text Mining Related Books

Low Resource Social Media Text Mining
Language: en
Pages: 67
Authors: Shriphani Palakodety
Categories: Computers
Type: BOOK - Published: 2021-10-01 - Publisher: Springer Nature

DOWNLOAD EBOOK

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 I
Low Resource Social Media Text Mining
Language: en
Pages: 60
Authors: Shriphani Palakodety
Categories: Computers
Type: BOOK - Published: 2021-10-03 - Publisher: Springer

DOWNLOAD EBOOK

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 I
Text Mining
Language: en
Pages: 189
Authors: Gabe Ignatow
Categories: Social Science
Type: BOOK - Published: 2016-04-20 - Publisher: SAGE Publications

DOWNLOAD EBOOK

Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and
Speech and Language Technologies for Low-Resource Languages
Language: en
Pages: 470
Authors: Bharathi Raja Chakravarthi
Categories:
Type: BOOK - Published: - Publisher: Springer Nature

DOWNLOAD EBOOK

Natural Language Processing for Social Media
Language: en
Pages: 197
Authors: Atefeh Farzindar
Categories: Computers
Type: BOOK - Published: 2017-12-15 - Publisher: Morgan & Claypool Publishers

DOWNLOAD EBOOK

In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been incre