{"id":"https://openalex.org/W4396833069","doi":"https://doi.org/10.1145/3613905.3637133","title":"Evaluating Interactive Topic Models in Applied Settings","display_name":"Evaluating Interactive Topic Models in Applied Settings","publication_year":2024,"publication_date":"2024-05-11","ids":{"openalex":"https://openalex.org/W4396833069","doi":"https://doi.org/10.1145/3613905.3637133"},"language":"en","primary_location":{"id":"doi:10.1145/3613905.3637133","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613905.3637133","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613905.3637133","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3613905.3637133","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061986206","display_name":"Sally Gao","orcid":"https://orcid.org/0009-0005-5727-0102"},"institutions":[{"id":"https://openalex.org/I68384125","display_name":"Thomson Reuters (United States)","ror":"https://ror.org/00m7gt169","country_code":"US","type":"company","lineage":["https://openalex.org/I68384125"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sally Gao","raw_affiliation_strings":["Thomson Reuters Labs, United States"],"raw_orcid":"https://orcid.org/0009-0005-5727-0102","affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, United States","institution_ids":["https://openalex.org/I68384125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042650113","display_name":"Milda Norkut\u0117","orcid":"https://orcid.org/0000-0002-7817-1171"},"institutions":[{"id":"https://openalex.org/I4210121390","display_name":"Thomson Reuters (Switzerland)","ror":"https://ror.org/02kh7ez55","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210121390","https://openalex.org/I68384125"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Milda Norkute","raw_affiliation_strings":["Thomson Reuters Labs, Switzerland"],"raw_orcid":"https://orcid.org/0000-0002-7817-1171","affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, Switzerland","institution_ids":["https://openalex.org/I4210121390"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101959107","display_name":"Abhinav Agrawal","orcid":"https://orcid.org/0009-0006-2742-4569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abhinav Agrawal","raw_affiliation_strings":["Thomson Reuters Labs, India"],"raw_orcid":"https://orcid.org/0009-0006-2742-4569","affiliations":[{"raw_affiliation_string":"Thomson Reuters Labs, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.9097,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.95648611,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9283772706985474},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8507777452468872},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8155916333198547},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5541726350784302},{"id":"https://openalex.org/keywords/human-in-the-loop","display_name":"Human-in-the-loop","score":0.5381691455841064},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5200154781341553},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5117356777191162},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49498337507247925},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.49463316798210144},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.48960286378860474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48638418316841125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.363753080368042},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14138972759246826}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9283772706985474},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8507777452468872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8155916333198547},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5541726350784302},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.5381691455841064},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5200154781341553},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5117356777191162},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49498337507247925},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.49463316798210144},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.48960286378860474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48638418316841125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.363753080368042},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14138972759246826},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3613905.3637133","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613905.3637133","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613905.3637133","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3613905.3637133","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3613905.3637133","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3613905.3637133","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Extended Abstracts of the CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396833069.pdf","grobid_xml":"https://content.openalex.org/works/W4396833069.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W76930416","https://openalex.org/W1969486090","https://openalex.org/W1979290264","https://openalex.org/W2001082470","https://openalex.org/W2158085718","https://openalex.org/W2160507407","https://openalex.org/W2250239827","https://openalex.org/W2292818183","https://openalex.org/W2595494611","https://openalex.org/W2625714162","https://openalex.org/W2737946880","https://openalex.org/W2794125445","https://openalex.org/W2808151906","https://openalex.org/W2911966198","https://openalex.org/W2950015742","https://openalex.org/W3010468287","https://openalex.org/W3178133156","https://openalex.org/W4287100616"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W2905433371","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W2963326959"],"abstract_inverted_index":{"Topic":[0],"modeling":[1,19],"is":[2],"a":[3,13,39,66],"text":[4],"analysis":[5],"technique":[6],"for":[7,144],"automatically":[8],"discovering":[9],"common":[10],"themes":[11],"in":[12,56,106,138],"collection":[14],"of":[15,30,47,72],"documents.":[16],"\u201cHuman-in-the-loop\u201d":[17],"topic":[18,31,135],"(HLTM)":[20],"allows":[21],"domain":[22],"experts":[23],"to":[24,43,112],"steer":[25],"and":[26,53,85,92,103,132,141],"adjust":[27],"the":[28,45,83,115],"creation":[29],"models.":[32],"In":[33],"this":[34],"case":[35],"study,":[36],"we":[37,81,124],"use":[38],"custom-built":[40],"HLTM":[41,146],"interface":[42],"assess":[44],"impact":[46],"human":[48,90,98],"refinement":[49,99],"on":[50,114],"model":[51,121,136],"interpretability":[52,102],"predictive":[54,104],"performance":[55,105],"collaboration":[57],"with":[58],"an":[59,76],"analytics":[60],"team":[61],"within":[62],"our":[63],"organization.":[64],"Using":[65],"small":[67],"dataset":[68],"(\u2248":[69],"12k":[70],"documents)":[71],"responses":[73],"drawn":[74],"from":[75],"organizational":[77],"employee":[78],"satisfaction":[79],"survey,":[80],"compare":[82],"pre-":[84],"post-refinement":[86],"models":[87],"using":[88],"both":[89],"judgments":[91],"automated":[93],"metrics.":[94],"We":[95],"find":[96],"that":[97,126],"can":[100],"enhance":[101],"some":[107],"cases,":[108],"but":[109],"may":[110],"lead":[111],"overfitting":[113],"training":[116],"data,":[117],"which":[118],"negatively":[119],"impacts":[120],"quality.":[122],"Furthermore,":[123],"observe":[125],"existing":[127],"evaluation":[128],"methods":[129],"don\u2019t":[130],"sufficiently":[131],"clearly":[133],"capture":[134],"quality":[137],"applied":[139],"settings,":[140],"propose":[142],"guidance":[143],"further":[145],"tool":[147],"development.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
