Making Sense of Large Social Media Corpora [electronic resource] : Keywords, Topics, Sentiment, and Hashtags in the Coronavirus Twitter Corpus / by Antonio Moreno-Ortiz.

За: Інтелектуальна відповідальність: Вид матеріалу: Текст Публікація: Cham : Springer Nature Switzerland : Imprint: Palgrave Macmillan, 2024Видання: 1st ed. 2024Опис: XII, 192 p. 105 illus., 102 illus. in color. online resourceТип вмісту:
  • text
Тип засобу:
  • computer
Тип носія:
  • online resource
ISBN:
  • 9783031527197
Тематика(и): Додаткові фізичні формати: Printed edition:: Немає назви; Printed edition:: Немає назви; Printed edition:: Немає назвиДесяткова класифікація Дьюї:
  • 407.21 23
Класифікація Бібліотеки Конгресу:
  • P29.52-41.22
Електронне місцезнаходження та доступ:
Вміст:
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.
У: Springer Nature eBookЗведення: 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.
Тип одиниці:
Мітки з цієї бібліотеки: Немає міток з цієї бібліотеки для цієї назви. Ввійдіть, щоб додавати мітки.
Оцінки зірочками
    Середня оцінка: 0.0 (0 голос.)
Немає реальних примірників для цього запису

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.

Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.

No reading system accessibility options actively disabled

Publisher contact for further accessibility information: accessibilitysupport@springernature.com

Немає коментарів для цієї одиниці.

для можливості публікувати коментарі.