{"id":"https://openalex.org/W4283258403","doi":"https://doi.org/10.1145/3529836.3529903","title":"Mixed network sentiment analysis combining sentiment features and multiple attention","display_name":"Mixed network sentiment analysis combining sentiment features and multiple attention","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283258403","doi":"https://doi.org/10.1145/3529836.3529903"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529903","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","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/A5025452122","display_name":"Siqi Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I167274908","display_name":"Guangxi University for Nationalities","ror":"https://ror.org/0495efn48","country_code":"CN","type":"education","lineage":["https://openalex.org/I167274908"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Siqi Zhan","raw_affiliation_strings":["Guangxi University for Nationalities, College of Electronic Information, China"],"affiliations":[{"raw_affiliation_string":"Guangxi University for Nationalities, College of Electronic Information, China","institution_ids":["https://openalex.org/I167274908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061782682","display_name":"Donghong Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I167274908","display_name":"Guangxi University for Nationalities","ror":"https://ror.org/0495efn48","country_code":"CN","type":"education","lineage":["https://openalex.org/I167274908"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghong Qin","raw_affiliation_strings":["Guangxi University for Nationalities, School of Artificial Intelligence, China"],"affiliations":[{"raw_affiliation_string":"Guangxi University for Nationalities, School of Artificial Intelligence, China","institution_ids":["https://openalex.org/I167274908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016106112","display_name":"Zhizhan Xu","orcid":"https://orcid.org/0000-0001-7062-8803"},"institutions":[{"id":"https://openalex.org/I167274908","display_name":"Guangxi University for Nationalities","ror":"https://ror.org/0495efn48","country_code":"CN","type":"education","lineage":["https://openalex.org/I167274908"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhizhan Xu","raw_affiliation_strings":["Guangxi University for Nationalities, College of Electronic Information, China"],"affiliations":[{"raw_affiliation_string":"Guangxi University for Nationalities, College of Electronic Information, China","institution_ids":["https://openalex.org/I167274908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5025452122"],"corresponding_institution_ids":["https://openalex.org/I167274908"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0544828,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2021","issue":null,"first_page":"205","last_page":"212"},"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/T10028","display_name":"Topic Modeling","score":0.9976000189781189,"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":0.9959999918937683,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.8533201217651367},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8468201160430908},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.727953314781189},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6713548302650452},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6125378012657166},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6020437479019165},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5148217082023621},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4701507091522217},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24373841285705566}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8533201217651367},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8468201160430908},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.727953314781189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6713548302650452},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6125378012657166},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6020437479019165},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5148217082023621},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4701507091522217},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24373841285705566},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529903","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.49000000953674316}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2925795486","https://openalex.org/W2945943453","https://openalex.org/W3011384704","https://openalex.org/W3015647315","https://openalex.org/W3088886232","https://openalex.org/W3121481086","https://openalex.org/W3137015680","https://openalex.org/W3213142362"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W3026913501"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,67,86,103,133],"solve":[3],"the":[4,52,69,73,80,88,93,105,119,128,135,144,151,154],"problem":[5],"of":[6,9,39,72,92,107,122,137,153],"low":[7],"attention":[8,37,101],"some":[10],"emotional":[11,46,56],"words":[12,75],"in":[13,19],"sentiment":[14,30,40,74,83,108],"analysis":[15,31,109],"tasks":[16],"and":[17,54,76,82,95,111,127,150],"difficulty":[18],"capturing":[20],"long-distance":[21],"dependence":[22],"between":[23],"sentences,":[24],"this":[25],"paper":[26],"proposes":[27],"a":[28,43],"mixed":[29],"network":[32,114,124],"(DB-BGA-CNN)":[33],"that":[34],"integrates":[35],"multiple":[36,100,141],"mechanisms":[38,102],"words.":[41],"First,":[42],"more":[44],"targeted":[45],"dictionary":[47,84],"is":[48,62,116,131],"obtained":[49],"by":[50],"expanding":[51],"dictionary,":[53],"an":[55],"word":[57,70],"selection":[58],"segmentation":[59],"algorithm":[60],"(DSS)":[61],"designed.":[63],"Secondly,":[64],"use":[65,99],"Bert":[66],"encode":[68],"vector":[71],"phrases":[77],"selected":[78],"from":[79],"sentence":[81],"respectively":[85],"obtain":[87],"deep":[89],"semantic":[90],"features":[91],"text":[94],"perform":[96],"fusion.":[97],"Then,":[98],"realize":[104],"enhancement":[106],"capabilities,":[110],"discuss":[112],"which":[113],"effect":[115],"better;":[117],"finally,":[118],"output":[120],"vectors":[121],"each":[123],"are":[125],"merged,":[126],"activation-pooling":[129],"layer":[130],"used":[132],"avoid":[134],"occurrence":[136],"overfitting.":[138],"Compared":[139],"with":[140],"existing":[142],"models,":[143],"proposed":[145],"model":[146,156],"shows":[147],"better":[148],"performance,":[149],"accuracy":[152],"optimal":[155],"reaches":[157],"95.80%.":[158]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}