| Predicate |
Object |
| contentType |
Review|Journal Article |
| issn |
0049-089X |
| pageRange |
102784- |
| publicationName |
Social Science Research |
| startingPage |
102784 |
| bibliographicCitation |
Macanovic A. Text mining for social science - The state and the future of computational text analysis in sociology. Soc Sci Res. 2022 Nov;108():102784. doi: 10.1016/j.ssresearch.2022.102784. PMID: 36334929. |
| creator |
http://rdf.ncbi.nlm.nih.gov/pubchem/author/ORCID_0000-0003-0800-5271 |
| date |
202211 |
| identifier |
https://doi.org/10.1016/j.ssresearch.2022.102784
https://pubmed.ncbi.nlm.nih.gov/36334929 |
| isPartOf |
http://rdf.ncbi.nlm.nih.gov/pubchem/journal/20467
https://portal.issn.org/resource/ISSN/0049-089X |
| language |
English |
| source |
https://pubmed.ncbi.nlm.nih.gov/
https://www.crossref.org/ |
| title |
Text mining for social science – The state and the future of computational text analysis in sociology |
| hasPrimarySubjectTerm |
http://id.nlm.nih.gov/mesh/D007802
http://id.nlm.nih.gov/mesh/D057225Q000379 |
| hasSubjectTerm |
http://id.nlm.nih.gov/mesh/D006801
http://id.nlm.nih.gov/mesh/D012961
http://id.nlm.nih.gov/mesh/D012942 |