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Making sense of large social media corporakeywords, topics, sentiment, and hashtags in the Coronavirus Twitter corpus /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Making sense of large social media corporaby Antonio Moreno-Ortiz.
Reminder of title:	keywords, topics, sentiment, and hashtags in the Coronavirus Twitter corpus /
Author:	Moreno-Ortiz, Antonio.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xii, 192 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Social mediaData processing.
Online resource:	https://doi.org/10.1007/978-3-031-52719-7
ISBN:	9783031527197$q(electronic bk.)

Making sense of large social media corporakeywords, topics, sentiment, and hashtags in the Coronavirus Twitter corpus /
Moreno-Ortiz, Antonio.

Making sense of large social media corporakeywords, topics, sentiment, and hashtags in the Coronavirus Twitter corpus /[electronic resource] :by Antonio Moreno-Ortiz. - Cham :Springer Nature Switzerland :2024. - xii, 192 p. :ill. (chiefly col.), digital ;24 cm.

Chapter 1 - Introduction -- Chapter 2 Managing large Twitter datasets -- Chapter 3. Keywords -- Chapter 4. Topics -- Chapter 5. Sentiment -- Chapter 6. Entities -- Chapter 7. Other social media semantic items: hashtags and emojis -- Chapter 8. Lessons learned.

Open access.

This open access book offers a comprehensive overview of available techniques and approaches to explore large social media corpora, using as an illustrative case study the Coronavirus Twitter corpus. First, the author describes in detail a number of methods, strategies, and tools that can be used to access, manage, and explore large Twitter/X corpora, including both user-friendly applications and more advanced methods that involve the use of data management skills and custom programming scripts. He goes on to show how these tools and methods are applied to explore one of the largest Twitter datasets on the COVID-19 pandemic publicly released, covering the two years when the pandemic had the strongest impact on society. Specifically, keyword extraction, topic modelling, sentiment analysis, and hashtag analysis methods are described, contrasted, and applied to extract information from the Coronavirus Twitter Corpus. The book will be of interest to students and researchers in fields that make use of big data to address societal and linguistic concerns, including corpus linguistics, sociology, psychology, and economics. Antonio Moreno-Ortiz is a lecturer at the Faculty of Arts of the University of Malaga, Spain.

ISBN: 9783031527197$q(electronic bk.)

Standard No.: 10.1007/978-3-031-52719-7doiSubjects--Uniform Titles:

Twitter.
Subjects--Topical Terms:

902663
Social media
--Data processing.

LC Class. No.: HM742 / .M67 2024

Dewey Class. No.: 302.231072

Making sense of large social media corporakeywords, topics, sentiment, and hashtags in the Coronavirus Twitter corpus /
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