{"id":"https://openalex.org/W4312970983","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892934","title":"Interactive Double Graph Convolutional Networks for Aspect-based Sentiment Analysis","display_name":"Interactive Double Graph Convolutional Networks for Aspect-based Sentiment Analysis","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312970983","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892934"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892934","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5100422559","display_name":"Xue Wang","orcid":"https://orcid.org/0000-0002-2381-9145"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Wang","raw_affiliation_strings":["School of information science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of information science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716216","display_name":"Peiyu Liu","orcid":"https://orcid.org/0000-0002-2905-5473"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiyu Liu","raw_affiliation_strings":["School of information science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of information science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003571051","display_name":"Zhenfang Zhu","orcid":"https://orcid.org/0000-0002-7217-3109"},"institutions":[{"id":"https://openalex.org/I4210099312","display_name":"Shandong Jiaotong University","ror":"https://ror.org/01848hk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenfang Zhu","raw_affiliation_strings":["School of Information Science and Electrical Engineering, Shandong Jiaotong University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Electrical Engineering, Shandong Jiaotong University,Jinan,China","institution_ids":["https://openalex.org/I4210099312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079882994","display_name":"Ran Lu","orcid":"https://orcid.org/0000-0002-5160-3122"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Lu","raw_affiliation_strings":["School of information science and Engineering, Shandong Normal University,Jinan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of information science and Engineering, Shandong Normal University,Jinan,China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/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":0.9936000108718872,"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":0.9905999898910522,"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/computer-science","display_name":"Computer science","score":0.8756815195083618},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6347423195838928},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6030605435371399},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5932588577270508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5791231393814087},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5610349178314209},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5570039749145508},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5025582313537598},{"id":"https://openalex.org/keywords/dependency-graph","display_name":"Dependency graph","score":0.45338523387908936},{"id":"https://openalex.org/keywords/dependency-grammar","display_name":"Dependency grammar","score":0.43421247601509094},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.430090069770813},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.417649507522583},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3965861201286316},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14449581503868103}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8756815195083618},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6347423195838928},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6030605435371399},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5932588577270508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791231393814087},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5610349178314209},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5570039749145508},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5025582313537598},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.45338523387908936},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.43421247601509094},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.430090069770813},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.417649507522583},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3965861201286316},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14449581503868103},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892934","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1913954887","https://openalex.org/W2250539671","https://openalex.org/W2251124635","https://openalex.org/W2252057809","https://openalex.org/W2296438605","https://openalex.org/W2465978385","https://openalex.org/W2562607067","https://openalex.org/W2757541972","https://openalex.org/W2788610610","https://openalex.org/W2798304389","https://openalex.org/W2891778157","https://openalex.org/W2892094955","https://openalex.org/W2946515079","https://openalex.org/W2949161734","https://openalex.org/W2962808042","https://openalex.org/W2962824509","https://openalex.org/W2962946486","https://openalex.org/W2963168371","https://openalex.org/W2964164368","https://openalex.org/W2970583420","https://openalex.org/W2970748008","https://openalex.org/W2971220558","https://openalex.org/W2982455176","https://openalex.org/W2997013919","https://openalex.org/W3035529900","https://openalex.org/W3035740499","https://openalex.org/W3100060077","https://openalex.org/W3116312348","https://openalex.org/W3117670243","https://openalex.org/W3173982660","https://openalex.org/W3176719207","https://openalex.org/W6631943919","https://openalex.org/W6697136110"],"related_works":["https://openalex.org/W2251084681","https://openalex.org/W2098784136","https://openalex.org/W2968543375","https://openalex.org/W4288558800","https://openalex.org/W2888625260","https://openalex.org/W2953770453","https://openalex.org/W2179743027","https://openalex.org/W4285169662","https://openalex.org/W4285666902","https://openalex.org/W2994684392"],"abstract_inverted_index":{"Aspect-based":[0],"sentiment":[1,7],"analysis":[2],"aims":[3],"to":[4,24,82,105,119],"judge":[5],"the":[6,26,41,44,53,78,83,88,96,107,121,133],"polarity":[8],"of":[9,46,137],"specific":[10],"aspects":[11,29],"in":[12,63],"comments.":[13],"Recent":[14],"meth-ods":[15],"use":[16],"graph":[17,71,90,98,123],"neural":[18],"networks":[19,73],"based":[20],"on":[21,129],"dependency":[22,47,79],"trees":[23],"obtain":[25],"relationship":[27],"between":[28,110],"and":[30,52,86,135],"opinion":[31],"words":[32],"by":[33],"using":[34],"syntactic":[35,57,84,89],"information.":[36],"However,":[37],"these":[38,61],"models":[39],"ignore":[40],"situation":[42],"that":[43],"results":[45,128],"tree":[48,80],"parsing":[49],"are":[50],"incorrect":[51],"sentences":[54],"without":[55],"significant":[56],"structure.":[58],"To":[59],"solve":[60],"problems,":[62],"this":[64],"paper,":[65],"we":[66,94,114],"propose":[67],"an":[68],"interactive":[69,116],"double":[70],"convolution":[72,91,99],"(Inter-DGCN)":[74],"model.":[75,140],"We":[76],"reconstruct":[77],"according":[81],"in-formation":[85],"construct":[87,95],"module.":[92],"Moreover,":[93],"semantic":[97,108],"module,":[100],"which":[101],"uses":[102],"multi-head":[103],"self-attention":[104],"represent":[106],"correlation":[109],"words.":[111],"In":[112],"addition,":[113],"add":[115],"feature":[117],"fusion":[118],"learn":[120],"dual":[122],"convolutional":[124],"network":[125],"interactively.":[126],"Experimental":[127],"three":[130],"datasets":[131],"demonstrate":[132],"effectiveness":[134],"advancement":[136],"our":[138],"proposed":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}