Making sense of large social media corpora: keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Palgrave Macmillan
[2024]
|
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xii, 192 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9783031527180 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049735635 | ||
| 003 | DE-604 | ||
| 005 | 20240805 | ||
| 007 | t | ||
| 008 | 240610s2024 a||| |||| 00||| eng d | ||
| 020 | |a 9783031527180 |c Festeinband |9 978-3-031-52718-0 | ||
| 035 | |a (OCoLC)1450747754 | ||
| 035 | |a (DE-599)BVBBV049735635 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 | ||
| 100 | 1 | |a Moreno-Ortiz, Antonio |e Verfasser |0 (DE-588)1223121216 |4 aut | |
| 245 | 1 | 0 | |a Making sense of large social media corpora |b keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus |c Antonio Moreno-Ortiz |
| 264 | 1 | |a Cham, Switzerland |b Palgrave Macmillan |c [2024] | |
| 264 | 4 | |c © 2024 | |
| 300 | |a xii, 192 Seiten |b Illustrationen, Diagramme, Karten | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a 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 | |
| 650 | 0 | 7 | |a COVID-19 |0 (DE-588)1206347392 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Social Media |0 (DE-588)4639271-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Linguistik |0 (DE-588)4074250-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Hashtag |0 (DE-588)1164034995 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Twitter |g Softwareplattform |0 (DE-588)7660487-1 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Linguistics / Methodology | |
| 653 | 0 | |a Social media | |
| 653 | 0 | |a Communication in medicine | |
| 653 | 0 | |a Applied linguistics | |
| 653 | 0 | |a Research Methods in Language and Linguistics | |
| 653 | 0 | |a Social Media | |
| 653 | 0 | |a Health Communication | |
| 653 | 0 | |a Applied Linguistics | |
| 653 | 0 | |a Linguistique / Méthodologie | |
| 653 | 0 | |a Médias sociaux | |
| 653 | 0 | |a Communication en médecine | |
| 653 | 0 | |a Linguistique appliquée | |
| 653 | 0 | |a social media | |
| 653 | 0 | |a applied linguistics | |
| 689 | 0 | 0 | |a Social Media |0 (DE-588)4639271-3 |D s |
| 689 | 0 | 1 | |a Linguistik |0 (DE-588)4074250-7 |D s |
| 689 | 0 | 2 | |a Twitter |g Softwareplattform |0 (DE-588)7660487-1 |D s |
| 689 | 0 | 3 | |a Hashtag |0 (DE-588)1164034995 |D s |
| 689 | 0 | 4 | |a COVID-19 |0 (DE-588)1206347392 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-031-52719-7 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035077752 | |
Datensatz im Suchindex
| _version_ | 1806595822891040768 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Moreno-Ortiz, Antonio |
| author_GND | (DE-588)1223121216 |
| author_facet | Moreno-Ortiz, Antonio |
| author_role | aut |
| author_sort | Moreno-Ortiz, Antonio |
| author_variant | a m o amo |
| building | Verbundindex |
| bvnumber | BV049735635 |
| ctrlnum | (OCoLC)1450747754 (DE-599)BVBBV049735635 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV049735635</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240805</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">240610s2024 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783031527180</subfield><subfield code="c">Festeinband</subfield><subfield code="9">978-3-031-52718-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1450747754</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049735635</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Moreno-Ortiz, Antonio</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1223121216</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Making sense of large social media corpora</subfield><subfield code="b">keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus</subfield><subfield code="c">Antonio Moreno-Ortiz</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham, Switzerland</subfield><subfield code="b">Palgrave Macmillan</subfield><subfield code="c">[2024]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xii, 192 Seiten</subfield><subfield code="b">Illustrationen, Diagramme, Karten</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">COVID-19</subfield><subfield code="0">(DE-588)1206347392</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Social Media</subfield><subfield code="0">(DE-588)4639271-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Linguistik</subfield><subfield code="0">(DE-588)4074250-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Hashtag</subfield><subfield code="0">(DE-588)1164034995</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Twitter</subfield><subfield code="g">Softwareplattform</subfield><subfield code="0">(DE-588)7660487-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Linguistics / Methodology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Social media</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication in medicine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Applied linguistics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Research Methods in Language and Linguistics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Social Media</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Health Communication</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Applied Linguistics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Linguistique / Méthodologie</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Médias sociaux</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Communication en médecine</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Linguistique appliquée</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">social media</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">applied linguistics</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Social Media</subfield><subfield code="0">(DE-588)4639271-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Linguistik</subfield><subfield code="0">(DE-588)4074250-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Twitter</subfield><subfield code="g">Softwareplattform</subfield><subfield code="0">(DE-588)7660487-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Hashtag</subfield><subfield code="0">(DE-588)1164034995</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="4"><subfield code="a">COVID-19</subfield><subfield code="0">(DE-588)1206347392</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-031-52719-7</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035077752</subfield></datafield></record></collection> |
| id | DE-604.BV049735635 |
| illustrated | Illustrated |
| indexdate | 2024-08-06T00:27:16Z |
| institution | BVB |
| isbn | 9783031527180 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035077752 |
| oclc_num | 1450747754 |
| open_access_boolean | |
| owner | DE-12 |
| owner_facet | DE-12 |
| physical | xii, 192 Seiten Illustrationen, Diagramme, Karten |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Palgrave Macmillan |
| record_format | marc |
| spelling | Moreno-Ortiz, Antonio Verfasser (DE-588)1223121216 aut Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus Antonio Moreno-Ortiz Cham, Switzerland Palgrave Macmillan [2024] © 2024 xii, 192 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier 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 COVID-19 (DE-588)1206347392 gnd rswk-swf Social Media (DE-588)4639271-3 gnd rswk-swf Linguistik (DE-588)4074250-7 gnd rswk-swf Hashtag (DE-588)1164034995 gnd rswk-swf Twitter Softwareplattform (DE-588)7660487-1 gnd rswk-swf Linguistics / Methodology Social media Communication in medicine Applied linguistics Research Methods in Language and Linguistics Social Media Health Communication Applied Linguistics Linguistique / Méthodologie Médias sociaux Communication en médecine Linguistique appliquée social media applied linguistics Social Media (DE-588)4639271-3 s Linguistik (DE-588)4074250-7 s Twitter Softwareplattform (DE-588)7660487-1 s Hashtag (DE-588)1164034995 s COVID-19 (DE-588)1206347392 s DE-604 Erscheint auch als Online-Ausgabe 978-3-031-52719-7 |
| spellingShingle | Moreno-Ortiz, Antonio Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus COVID-19 (DE-588)1206347392 gnd Social Media (DE-588)4639271-3 gnd Linguistik (DE-588)4074250-7 gnd Hashtag (DE-588)1164034995 gnd Twitter Softwareplattform (DE-588)7660487-1 gnd |
| subject_GND | (DE-588)1206347392 (DE-588)4639271-3 (DE-588)4074250-7 (DE-588)1164034995 (DE-588)7660487-1 |
| title | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus |
| title_auth | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus |
| title_exact_search | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus |
| title_full | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus Antonio Moreno-Ortiz |
| title_fullStr | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus Antonio Moreno-Ortiz |
| title_full_unstemmed | Making sense of large social media corpora keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus Antonio Moreno-Ortiz |
| title_short | Making sense of large social media corpora |
| title_sort | making sense of large social media corpora keywords topics sentiment and hashtags in the coronavirus twitter corpus |
| title_sub | keywords, topics, sentiment, and hashtags in the coronavirus twitter corpus |
| topic | COVID-19 (DE-588)1206347392 gnd Social Media (DE-588)4639271-3 gnd Linguistik (DE-588)4074250-7 gnd Hashtag (DE-588)1164034995 gnd Twitter Softwareplattform (DE-588)7660487-1 gnd |
| topic_facet | COVID-19 Social Media Linguistik Hashtag Twitter Softwareplattform |
| work_keys_str_mv | AT morenoortizantonio makingsenseoflargesocialmediacorporakeywordstopicssentimentandhashtagsinthecoronavirustwittercorpus |