{"id":"https://openalex.org/W2742863681","doi":"https://doi.org/10.1145/3106426.3109416","title":"A sentiment polarity classifier for regional event reputation analysis","display_name":"A sentiment polarity classifier for regional event reputation analysis","publication_year":2017,"publication_date":"2017-08-10","ids":{"openalex":"https://openalex.org/W2742863681","doi":"https://doi.org/10.1145/3106426.3109416","mag":"2742863681"},"language":"en","primary_location":{"id":"doi:10.1145/3106426.3109416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3109416","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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 International Conference on Web Intelligence","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/A5019617051","display_name":"Tatsuya Ohbe","orcid":null},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Ohbe","raw_affiliation_strings":["Nagoya Institute of Technology, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Institute of Technology, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025626453","display_name":"Tadachika Ozono","orcid":"https://orcid.org/0000-0003-1568-8832"},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tadachika Ozono","raw_affiliation_strings":["Nagoya Institute of Technology, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Institute of Technology, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040292190","display_name":"Toramatsu Shintani","orcid":null},"institutions":[{"id":"https://openalex.org/I197274945","display_name":"Nagoya Institute of Technology","ror":"https://ror.org/055yf1005","country_code":"JP","type":"education","lineage":["https://openalex.org/I197274945"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toramatsu Shintani","raw_affiliation_strings":["Nagoya Institute of Technology, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Institute of Technology, Aichi, Japan","institution_ids":["https://openalex.org/I197274945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3481,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6705286,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1207","last_page":"1213"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9997000098228455,"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":0.9997000098228455,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.992900013923645,"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":0.9904000163078308,"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/computer-science","display_name":"Computer science","score":0.7886331081390381},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6998504996299744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.68215411901474},{"id":"https://openalex.org/keywords/reputation","display_name":"Reputation","score":0.6773043870925903},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6667805910110474},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6568694114685059},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5768991112709045},{"id":"https://openalex.org/keywords/polarity","display_name":"Polarity (international relations)","score":0.4976966679096222},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4690476059913635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.448102205991745},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3835490942001343}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7886331081390381},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6998504996299744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.68215411901474},{"id":"https://openalex.org/C48798503","wikidata":"https://www.wikidata.org/wiki/Q877546","display_name":"Reputation","level":2,"score":0.6773043870925903},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6667805910110474},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6568694114685059},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5768991112709045},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.4976966679096222},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4690476059913635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.448102205991745},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3835490942001343},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C1491633281","wikidata":"https://www.wikidata.org/wiki/Q7868","display_name":"Cell","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3106426.3109416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3106426.3109416","pdf_url":null,"source":{"id":"https://openalex.org/S4306524158","display_name":"Proceedings of the International Conference on Web Intelligence","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 International Conference on Web Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1832693441","https://openalex.org/W2005482830","https://openalex.org/W2049434052","https://openalex.org/W2052635433","https://openalex.org/W2061760029","https://openalex.org/W2131744502","https://openalex.org/W2139982652","https://openalex.org/W2141790691","https://openalex.org/W2157331557","https://openalex.org/W2170414372","https://openalex.org/W2240185015","https://openalex.org/W2398854657","https://openalex.org/W2493916176","https://openalex.org/W2513262852","https://openalex.org/W2573149012","https://openalex.org/W2950133940","https://openalex.org/W2963626623"],"related_works":["https://openalex.org/W4392337488","https://openalex.org/W2102271161","https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W4313425421","https://openalex.org/W2941935829","https://openalex.org/W4388714791","https://openalex.org/W2361883455","https://openalex.org/W2952832228","https://openalex.org/W1984947604"],"abstract_inverted_index":{"It":[0],"is":[1],"important":[2],"to":[3,28,74,95],"analyze":[4,75],"the":[5,43,49,63,68,97,100,110,118,122,127,137,141],"reputation":[6,78],"or":[7],"demands":[8],"for":[9],"a":[10,15,71],"regional":[11,30,76,83],"event,":[12],"such":[13],"as":[14],"school":[16],"festival.":[17],"In":[18,89],"our":[19,87],"work,":[20],"we":[21,52,65,92],"use":[22],"sentiment":[23,35,101],"polarity":[24,36,102],"classification":[25,37,103,119],"in":[26,42,115],"order":[27],"coordinate":[29],"event":[31,77,84],"reputation.":[32],"We":[33,108,125],"proposed":[34,53],"based":[38,57,131],"on":[39,58],"bag-of-words":[40],"models":[41,56,114],"previous":[44],"works.":[45],"To":[46],"get":[47],"over":[48],"traditional":[50],"models,":[51],"several":[54],"classifier":[55],"deep":[59,105],"learning":[60,106],"models.":[61,107,143],"As":[62],"application,":[64],"also":[66],"described":[67,93],"overview":[69],"of":[70,82,99,112,117],"system":[72],"supports":[73],"and":[79,121],"an":[80],"example":[81],"analysis":[85],"using":[86,104],"system.":[88],"this":[90],"paper,":[91],"how":[94],"improve":[96],"performance":[98,111],"compared":[109],"four":[113,142],"terms":[116],"accuracy":[120],"training":[123],"speed.":[124],"found":[126],"Convolutional":[128],"Neural":[129],"Networks":[130],"model,":[132],"three":[133],"words":[134],"convolutions,":[135],"was":[136],"best":[138],"model":[139],"among":[140]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
