{"id":"https://openalex.org/W4284707793","doi":"https://doi.org/10.1145/3477495.3532676","title":"Beyond Opinion Mining","display_name":"Beyond Opinion Mining","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4284707793","doi":"https://doi.org/10.1145/3477495.3532676"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3532676","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3532676","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5009993083","display_name":"Reinald Kim Amplayo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113297","display_name":"Google (United Kingdom)","ror":"https://ror.org/024bc3e07","country_code":"GB","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210113297","https://openalex.org/I4210128969"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Reinald Kim Amplayo","raw_affiliation_strings":["Google Research, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Google Research, London, United Kingdom","institution_ids":["https://openalex.org/I4210113297"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052381890","display_name":"Arthur Bra\u017einskas","orcid":null},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Arthur Brazinskas","raw_affiliation_strings":["University of Edinburgh, Edinburgh, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Edinburgh, Edinburgh, United Kingdom","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054434349","display_name":"Yoshi Suhara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoshi Suhara","raw_affiliation_strings":["Grammarly, San Francisco, CA, USA"],"affiliations":[{"raw_affiliation_string":"Grammarly, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767232","display_name":"Xiaolan Wang","orcid":"https://orcid.org/0000-0003-3846-1753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaolan Wang","raw_affiliation_strings":["Megagon Labs, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Megagon Labs, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339927","display_name":"Bing Liu","orcid":"https://orcid.org/0000-0002-4096-6980"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5009993083"],"corresponding_institution_ids":["https://openalex.org/I4210113297"],"apc_list":null,"apc_paid":null,"fwci":1.0394,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.78081232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3447","last_page":"3450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9991999864578247,"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/automatic-summarization","display_name":"Automatic summarization","score":0.9625167846679688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8465582132339478},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6254293918609619},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6115233302116394},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5306970477104187},{"id":"https://openalex.org/keywords/purchasing","display_name":"Purchasing","score":0.5188247561454773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4900030195713043},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4746611416339874}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.9625167846679688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8465582132339478},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6254293918609619},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6115233302116394},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5306970477104187},{"id":"https://openalex.org/C2778813691","wikidata":"https://www.wikidata.org/wiki/Q1369832","display_name":"Purchasing","level":2,"score":0.5188247561454773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4900030195713043},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4746611416339874},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3532676","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3532676","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2966661","https://openalex.org/W2110693578","https://openalex.org/W2134854314","https://openalex.org/W2145071407","https://openalex.org/W2148404145","https://openalex.org/W2160660844","https://openalex.org/W2166706824","https://openalex.org/W2530746622","https://openalex.org/W2963223306","https://openalex.org/W2963721761","https://openalex.org/W2983771799","https://openalex.org/W2997669619","https://openalex.org/W3020873268","https://openalex.org/W3034226234","https://openalex.org/W3034352114","https://openalex.org/W3034383590","https://openalex.org/W3035043191","https://openalex.org/W3035576805","https://openalex.org/W3098648976","https://openalex.org/W3153239706","https://openalex.org/W3153621364","https://openalex.org/W3153949951","https://openalex.org/W3160369148","https://openalex.org/W3173759686","https://openalex.org/W3174600099","https://openalex.org/W3198269165","https://openalex.org/W4205169447","https://openalex.org/W4205184193","https://openalex.org/W4205742210","https://openalex.org/W4213038497","https://openalex.org/W6679641371"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W4306886878","https://openalex.org/W2973759123","https://openalex.org/W1517524280"],"abstract_inverted_index":{"Customer":[0],"reviews":[1,13],"are":[2,38],"vital":[3],"for":[4,40,138],"making":[5],"purchasing":[6],"decisions":[7],"in":[8,73,84,123],"the":[9,20,48,74,109,132],"Information":[10],"Age.":[11],"Such":[12],"can":[14,70],"be":[15,71,129],"automatically":[16],"summarized":[17],"to":[18],"provide":[19,116],"user":[21],"with":[22,108,131],"an":[23],"overview":[24,119],"of":[25,34],"opinions.":[26],"In":[27],"this":[28],"tutorial,":[29],"we":[30,45,54,99],"present":[31,56],"various":[32],"aspects":[33],"opinion":[35,58,124],"summarization":[36,59],"that":[37,134],"useful":[39,137],"researchers":[41],"and":[42,50,63,77,95,103,106,140],"practitioners.":[43],"First,":[44],"will":[46,55,66,100,115,128],"introduce":[47],"task":[49],"major":[51,121],"challenges.":[52],"Then,":[53],"existing":[57],"solutions,":[60],"both":[61,136],"pre-neural":[62],"neural.":[64],"We":[65],"discuss":[67,101],"how":[68],"summarizers":[69],"trained":[72],"unsupervised,":[75],"few-shot,":[76],"supervised":[78],"regimes.":[79],"Each":[80],"regime":[81],"has":[82],"roots":[83],"different":[85],"machine":[86],"learning":[87],"methods,":[88],"such":[89],"as":[90],"auto-encoding,":[91],"controllable":[92],"text":[93],"generation,":[94],"variational":[96],"inference.":[97],"Finally,":[98],"resources":[102],"evaluation":[104],"methods":[105],"conclude":[107],"future":[110],"directions.":[111],"This":[112],"three-hour":[113],"tutorial":[114],"a":[117],"comprehensive":[118],"over":[120],"advances":[122],"summarization.":[125],"The":[126],"listeners":[127],"well-equipped":[130],"knowledge":[133],"is":[135],"research":[139],"practical":[141],"applications.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}