{"id":"https://openalex.org/W2767313619","doi":"https://doi.org/10.1145/3132847.3132864","title":"Tone Analyzer for Online Customer Service","display_name":"Tone Analyzer for Online Customer Service","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2767313619","doi":"https://doi.org/10.1145/3132847.3132864","mag":"2767313619"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3132864","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049036241","display_name":"Peifeng Yin","orcid":"https://orcid.org/0000-0002-0109-7903"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peifeng Yin","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100462383","display_name":"Zhe Liu","orcid":"https://orcid.org/0000-0003-1313-8327"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhe Liu","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053418697","display_name":"Anbang Xu","orcid":"https://orcid.org/0009-0005-9707-7817"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anbang Xu","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074644024","display_name":"Taiga Nakamura","orcid":null},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taiga Nakamura","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5049036241"],"corresponding_institution_ids":["https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":0.5851,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76398676,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1887","last_page":"1895"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9943000078201294,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8812031149864197},{"id":"https://openalex.org/keywords/tone","display_name":"Tone (literature)","score":0.7944250106811523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7428961396217346},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.5198798179626465},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5099915266036987},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41139376163482666},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.40067586302757263},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3816957175731659},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3209071159362793},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.07054775953292847}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8812031149864197},{"id":"https://openalex.org/C2780583480","wikidata":"https://www.wikidata.org/wiki/Q1366327","display_name":"Tone (literature)","level":2,"score":0.7944250106811523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7428961396217346},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.5198798179626465},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5099915266036987},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41139376163482666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40067586302757263},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3816957175731659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3209071159362793},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.07054775953292847},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3132864","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W49069620","https://openalex.org/W1488195763","https://openalex.org/W1506246224","https://openalex.org/W1572240091","https://openalex.org/W1743243001","https://openalex.org/W1819932765","https://openalex.org/W1880262756","https://openalex.org/W1924133215","https://openalex.org/W1969486090","https://openalex.org/W1976426554","https://openalex.org/W2005311637","https://openalex.org/W2028904519","https://openalex.org/W2048330531","https://openalex.org/W2074909580","https://openalex.org/W2084046180","https://openalex.org/W2098062695","https://openalex.org/W2101101940","https://openalex.org/W2122683976","https://openalex.org/W2129692524","https://openalex.org/W2142349102","https://openalex.org/W2154051144","https://openalex.org/W2156693754","https://openalex.org/W2164309533","https://openalex.org/W2171468534","https://openalex.org/W2339343773","https://openalex.org/W2531638282","https://openalex.org/W2571842322","https://openalex.org/W2611049140","https://openalex.org/W3121590070"],"related_works":["https://openalex.org/W2888805565","https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W4293863151","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W3005513013"],"abstract_inverted_index":{"Emotion":[0],"analysis":[1],"of":[2,34,78],"online":[3,32],"customer":[4,14,35],"service":[5,36],"conservation":[6],"is":[7,73,103,126],"important":[8],"for":[9],"good":[10],"user":[11],"experience":[12],"and":[13,30,85,101],"satisfaction.":[15],"However,":[16],"conventional":[17],"metrics":[18],"do":[19],"not":[20],"fit":[21],"this":[22,26],"application":[23],"scenario.":[24],"In":[25,68,110],"work,":[27],"by":[28],"collecting":[29],"labeling":[31],"conversations":[33],"on":[37],"Twitter,":[38],"we":[39,56],"identify":[40,106],"8":[41],"new":[42],"metrics,":[43],"named":[44],"as":[45],"tones,":[46],"to":[47,64,105,128],"describe":[48],"emotional":[49],"information.":[50],"To":[51],"better":[52,114],"interpret":[53],"each":[54,70,130],"tone,":[55],"extend":[57],"the":[58,96],"Latent":[59],"Dirichlet":[60],"Allocation":[61],"(LDA)":[62],"model":[63],"Tone":[65],"LDA":[66],"(T-LDA).":[67],"T-LDA,":[69],"latent":[71],"topic":[72],"explicitly":[74],"associated":[75],"with":[76],"one":[77],"three":[79],"semantic":[80],"categories,":[81],"i.e.,":[82],"tone-related,":[83],"domain-specific":[84],"auxiliary.":[86],"By":[87],"integrating":[88],"tone":[89,120,131],"label":[90],"into":[91],"learning,":[92],"T-LDA":[93,112,133],"can":[94],"interfere":[95],"original":[97],"unsupervised":[98],"training":[99],"process":[100],"thus":[102],"able":[104],"representative":[107],"tone-related":[108],"words.":[109],"evaluation,":[111],"shows":[113],"performance":[115],"than":[116],"baselines":[117],"in":[118],"predicting":[119],"intensity.":[121],"Also,":[122],"a":[123],"case":[124],"study":[125],"conducted":[127],"analyze":[129],"via":[132],"output.":[134]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}