{"id":"https://openalex.org/W3005967578","doi":"https://doi.org/10.1109/iceei47359.2019.8988898","title":"Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting","display_name":"Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W3005967578","doi":"https://doi.org/10.1109/iceei47359.2019.8988898","mag":"3005967578"},"language":"en","primary_location":{"id":"doi:10.1109/iceei47359.2019.8988898","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceei47359.2019.8988898","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Electrical Engineering and Informatics (ICEEI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5041805300","display_name":"Annisa Nurul Azhar","orcid":null},"institutions":[{"id":"https://openalex.org/I134635517","display_name":"Bandung Institute of Technology","ror":"https://ror.org/00apj8t60","country_code":"ID","type":"education","lineage":["https://openalex.org/I134635517"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Annisa Nurul Azhar","raw_affiliation_strings":["School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","institution_ids":["https://openalex.org/I134635517"]},{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia","institution_ids":["https://openalex.org/I134635517"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031833654","display_name":"Masayu Leylia Khodra","orcid":"https://orcid.org/0000-0002-8765-8109"},"institutions":[{"id":"https://openalex.org/I134635517","display_name":"Bandung Institute of Technology","ror":"https://ror.org/00apj8t60","country_code":"ID","type":"education","lineage":["https://openalex.org/I134635517"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Masayu Leylia Khodra","raw_affiliation_strings":["School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","institution_ids":["https://openalex.org/I134635517"]},{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia","institution_ids":["https://openalex.org/I134635517"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013237241","display_name":"Arie Pratama Sutiono","orcid":null},"institutions":[{"id":"https://openalex.org/I134635517","display_name":"Bandung Institute of Technology","ror":"https://ror.org/00apj8t60","country_code":"ID","type":"education","lineage":["https://openalex.org/I134635517"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Arie Pratama Sutiono","raw_affiliation_strings":["School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung,Bandung,Indonesia","institution_ids":["https://openalex.org/I134635517"]},{"raw_affiliation_string":"School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia","institution_ids":["https://openalex.org/I134635517"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I134635517"],"apc_list":null,"apc_paid":null,"fwci":0.8747,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.80963904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9994999766349792,"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":0.9950000047683716,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8208440542221069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7671558856964111},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.7354865670204163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6957643032073975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6366979479789734},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6204783320426941},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.619449257850647},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5572658777236938},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5252009034156799},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5100851655006409},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.48583003878593445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4418726861476898},{"id":"https://openalex.org/keywords/hamming-code","display_name":"Hamming code","score":0.43180012702941895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3544952869415283},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.08838927745819092},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.07769650220870972}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8208440542221069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7671558856964111},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.7354865670204163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6957643032073975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6366979479789734},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6204783320426941},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.619449257850647},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5572658777236938},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5252009034156799},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5100851655006409},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.48583003878593445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4418726861476898},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.43180012702941895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3544952869415283},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.08838927745819092},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.07769650220870972},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceei47359.2019.8988898","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceei47359.2019.8988898","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Electrical Engineering and Informatics (ICEEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1999954155","https://openalex.org/W2108646579","https://openalex.org/W2114315281","https://openalex.org/W2146241755","https://openalex.org/W2164672510","https://openalex.org/W2295598076","https://openalex.org/W2500816152","https://openalex.org/W2734389934","https://openalex.org/W2739312105","https://openalex.org/W2765747616","https://openalex.org/W2900595193","https://openalex.org/W2949541494","https://openalex.org/W3102476541","https://openalex.org/W4211186029","https://openalex.org/W6636510571","https://openalex.org/W6742076767"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Aspect":[0],"categorization":[1,34],"is":[2,11],"a":[3,12],"subtask":[4],"in":[5,64],"aspect-based":[6],"sentiment":[7],"analysis.":[8],"This":[9],"task":[10,15,35],"multi-label":[13,42],"classification":[14],"since":[16],"each":[17],"document":[18],"may":[19],"belong":[20],"to":[21,84],"more":[22],"than":[23],"one":[24],"aspect":[25,33],"categories.":[26],"In":[27],"this":[28,65],"paper,":[29],"we":[30],"divided":[31],"the":[32,59],"into":[36],"two":[37],"subtasks,":[38],"feature":[39,51],"extraction":[40],"and":[41,53,78,95],"classification.":[43],"We":[44],"use":[45],"Convolutional":[46],"Neural":[47],"Network":[48],"(CNN)":[49],"as":[50,58],"extractor":[52],"Extreme":[54],"Gradient":[55],"Boosting":[56],"(XGBoost)":[57],"top-level":[60],"classifier.":[61],"Dataset":[62],"used":[63],"paper":[66],"consists":[67],"of":[68],"9450":[69],"hotel":[70],"reviews.":[71],"Our":[72],"model":[73],"achieved":[74],"higher":[75],"F1-score,":[76],"0.9316,":[77],"lower":[79],"Hamming":[80],"Loss,":[81],"0.02667,":[82],"compared":[83],"our":[85],"baselines":[86],"which":[87],"are":[88],"CNN-LSTM":[89],"(F1":[90,97],"0.9198":[91],"&":[92,99],"loss":[93,100],"0.03217)":[94],"CNN-SVM":[96],"0.9295":[98],"0.02719).":[101]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}