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PMC:7143846 / 6803-9107 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
91 197-204 Disease denotes anxiety MESH:D001007
95 1252-1278 Disease denotes deep learning technologies MESH:D007859
96 1312-1319 Disease denotes anxiety MESH:D001007
97 1321-1331 Disease denotes depression MESH:D000275
101 1790-1797 Disease denotes anxiety MESH:D001007
102 1799-1809 Disease denotes depression MESH:D000275
103 2093-2101 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T30 197-204 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T32 1312-1331 Disease denotes anxiety, depression http://purl.obolibrary.org/obo/MONDO_0041086
T33 1312-1319 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T35 1321-1331 Disease denotes depression http://purl.obolibrary.org/obo/MONDO_0002050
T36 1790-1809 Disease denotes anxiety, depression http://purl.obolibrary.org/obo/MONDO_0041086
T37 1790-1797 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T39 1799-1809 Disease denotes depression http://purl.obolibrary.org/obo/MONDO_0002050
T40 2093-2101 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T35 487-489 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T36 748-755 http://purl.obolibrary.org/obo/BFO_0000030 denotes objects
T37 1135-1141 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T38 1581-1582 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T39 1668-1671 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T40 2150-2154 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T41 2213-2215 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T2 1520-1529 Chemical denotes indicator http://purl.obolibrary.org/obo/CHEBI_47867

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 197-204 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T8 1312-1319 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T9 1321-1331 Phenotype denotes depression http://purl.obolibrary.org/obo/HP_0000716
T10 1790-1797 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T11 1799-1809 Phenotype denotes depression http://purl.obolibrary.org/obo/HP_0000716

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T10 332-348 http://purl.obolibrary.org/obo/GO_0048731 denotes system developed
T11 515-527 http://purl.obolibrary.org/obo/GO_0035282 denotes segmentation
T12 600-612 http://purl.obolibrary.org/obo/GO_0035282 denotes segmentation
T13 1257-1265 http://purl.obolibrary.org/obo/GO_0007612 denotes learning

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T51 0-4 Sentence denotes 2.2.
T52 5-55 Sentence denotes Measurement of Psychological Traits and Procedures
T53 56-309 Sentence denotes In this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.
T54 310-574 Sentence denotes We employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23].
T55 575-905 Sentence denotes We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23].
T56 906-975 Sentence denotes These lexical features were data sources for word frequency analysis.
T57 976-1154 Sentence denotes After feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users.
T58 1155-1446 Sentence denotes These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on.
T59 1447-1541 Sentence denotes Figure 1 portrays the procedure from feature extraction to psychological indicator prediction.
T60 1542-1630 Sentence denotes All the prediction models have reached a moderate correlation with questionnaire scores.
T61 1631-1712 Sentence denotes The feasibility of predictive models has been repeatedly demonstrated [26,27,28].
T62 1713-1978 Sentence denotes We calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages.
T63 1979-2304 Sentence denotes We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA.

2_test

Id Subject Object Predicate Lexical cue
32204411-27322382-49451143 570-572 27322382 denotes 23
32204411-27322382-49451144 901-903 27322382 denotes 23
32204411-28059682-49451145 1708-1710 28059682 denotes 28
T70343 570-572 27322382 denotes 23
T61461 901-903 27322382 denotes 23
T27610 1708-1710 28059682 denotes 28