{"id":"https://openalex.org/W2811213997","doi":"https://doi.org/10.1109/fskd.2017.8392953","title":"Support vector machine equipped with deep convolutional features for product reviews classification","display_name":"Support vector machine equipped with deep convolutional features for product reviews classification","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2811213997","doi":"https://doi.org/10.1109/fskd.2017.8392953","mag":"2811213997"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8392953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5007011277","display_name":"Shangdi Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shangdi Sun","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000141628","display_name":"Xiaodong Gu","orcid":"https://orcid.org/0000-0002-7096-1830"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Gu","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007011277"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.39,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73052365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"130","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8198034763336182},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7919663190841675},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7636648416519165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6781244277954102},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5601208209991455},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5498723983764648},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.527123749256134},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5161882042884827},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.467303991317749},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4287046790122986},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41831305623054504},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39044860005378723},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37853649258613586},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.22392913699150085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8198034763336182},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7919663190841675},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7636648416519165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6781244277954102},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5601208209991455},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5498723983764648},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.527123749256134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5161882042884827},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.467303991317749},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4287046790122986},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41831305623054504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39044860005378723},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37853649258613586},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.22392913699150085},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8392953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W145476170","https://openalex.org/W1508567213","https://openalex.org/W1581485226","https://openalex.org/W2062118960","https://openalex.org/W2097117768","https://openalex.org/W2109634664","https://openalex.org/W2118020653","https://openalex.org/W2118585731","https://openalex.org/W2131876387","https://openalex.org/W2133280805","https://openalex.org/W2140321362","https://openalex.org/W2144211451","https://openalex.org/W2149684865","https://openalex.org/W2150098611","https://openalex.org/W2154359981","https://openalex.org/W2155454737","https://openalex.org/W2158899491","https://openalex.org/W2166706824","https://openalex.org/W2168893862","https://openalex.org/W2251939518","https://openalex.org/W2952230511","https://openalex.org/W6605963037","https://openalex.org/W6630570293","https://openalex.org/W6634901647","https://openalex.org/W6676621845","https://openalex.org/W6677656871","https://openalex.org/W6679915538","https://openalex.org/W6682304300","https://openalex.org/W6682839988","https://openalex.org/W6683738474","https://openalex.org/W6691459498"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W4205463238","https://openalex.org/W259157601","https://openalex.org/W3186997021","https://openalex.org/W2997097677","https://openalex.org/W4308088897","https://openalex.org/W4200618314","https://openalex.org/W2771357047"],"abstract_inverted_index":{"Online":[0],"shopping":[1],"websites":[2],"usually":[3,74,134],"ask":[4],"their":[5],"customers":[6],"to":[7,36,43,49,59],"review":[8],"the":[9,22,51,63,136,170,182,204],"products":[10],"and":[11,19,82,97,142,198,214],"associated":[12],"services.":[13],"As":[14],"e-commerce":[15],"is":[16,30,114,166],"becoming":[17],"more":[18,20],"popular,":[21],"number":[23],"of":[24,62,122,138,174,186],"customer":[25,35],"reviews":[26,40,55,157,193],"grows":[27],"rapidly.":[28],"It":[29],"difficult":[31],"for":[32,155],"a":[33,45],"potential":[34],"read":[37],"all":[38],"original":[39,151],"in":[41,67],"order":[42],"make":[44],"decision":[46],"on":[47,135,190],"whether":[48],"buy":[50],"product.":[52],"So":[53],"product":[54,156,192],"classification":[56,184,209],"has":[57,75,207],"grown":[58],"be":[60],"one":[61],"hottest":[64],"research":[65],"areas":[66],"sentiment":[68,72],"analysis.":[69],"In":[70,145],"practice,":[71],"analysis":[73],"two":[76],"methods:":[77],"Support":[78],"Vector":[79],"Machine":[80],"(SVM)":[81],"Convolutional":[83],"Neural":[84],"Network":[85],"(CNN).":[86],"The":[87,100],"former":[88],"uses":[89,102],"one-hot":[90,112],"representation":[91,104,113,128],"as":[92,108],"features":[93,121,165,177],"with":[94,111,126,162,181],"simple":[95],"network":[96],"competitive":[98],"results.":[99],"latter":[101],"distributed":[103,127],"or":[105],"word":[106],"embedding":[107],"features.":[109],"SVM":[110,159,187],"fast":[115],"but":[116,133],"may":[117],"not":[118],"extract":[119],"suitable":[120],"specific":[123],"texts.":[124],"CNN":[125,180,213],"can":[129],"get":[130],"impressive":[131],"results":[132],"basis":[137],"an":[139,150],"extremely":[140],"huge":[141],"complex":[143],"network.":[144],"this":[146],"paper,":[147],"we":[148],"proposed":[149,205],"architecture":[152],"called":[153],"CNN-SVM":[154],"classification.":[158],"classifier":[160],"equipped":[161],"deep":[163,175],"convolutional":[164,176],"utilized,":[167],"which":[168],"combines":[169],"terrific":[171],"discriminative":[172],"capability":[173],"learnt":[178],"by":[179],"excellent":[183],"performance":[185],"classifier.":[188],"Experiments":[189],"our":[191],"datasets":[194],"(Amazon":[195],"Smartphone":[196],"Review":[197],"Taobao":[199],"Skirt":[200],"Review),":[201],"show":[202],"that":[203],"method":[206],"higher":[208],"accuracy":[210],"than":[211],"plain":[212],"SVM.":[215]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}