{"id":"https://openalex.org/W2767201616","doi":"https://doi.org/10.48550/arxiv.1709.09885","title":"Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network","display_name":"Sentiment Classification with Word Attention based on Weakly Supervised Learning with a Convolutional Neural Network","publication_year":2017,"publication_date":"2017-09-28","ids":{"openalex":"https://openalex.org/W2767201616","doi":"https://doi.org/10.48550/arxiv.1709.09885","mag":"2767201616"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1709.09885","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.09885","pdf_url":"https://arxiv.org/pdf/1709.09885","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1709.09885","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024050253","display_name":"Gichang Lee","orcid":"https://orcid.org/0000-0001-8387-0981","observed_orcids":["https://orcid.org/0000-0001-8387-0981"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Gichang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101128910","display_name":"Jae-Yun Jeong","orcid":"https://orcid.org/0009-0005-3281-5858","observed_orcids":["https://orcid.org/0009-0005-3281-5858"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Jaeyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101726173","display_name":"Seungwan Seo","orcid":"https://orcid.org/0000-0001-5204-3350","observed_orcids":["https://orcid.org/0000-0001-5204-3350"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Seungwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050791232","display_name":"CzangYeob Kim","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, CzangYeob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059650940","display_name":"Pilsung Kang","orcid":"https://orcid.org/0000-0001-7663-3937","observed_orcids":["https://orcid.org/0000-0001-7663-3937"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Pilsung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":1.0,"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":1.0,"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":0.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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-classification","display_name":"sentiment classification","score":0.8989999890327454},{"id":"https://openalex.org/keywords/weakly-supervised-learning","display_name":"weakly supervised learning","score":0.9900000095367432},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8769999742507935},{"id":"https://openalex.org/keywords/class-activation-maps","display_name":"class activation maps","score":0.7730000019073486},{"id":"https://openalex.org/keywords/sentence-level-sentiment","display_name":"sentence-level sentiment","score":0.4740000069141388},{"id":"https://openalex.org/keywords/movie-reviews","display_name":"movie reviews","score":0.7390000224113464}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7383893132209778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.668147087097168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657729983329773},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6442419290542603},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5417405366897583},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5150265693664551},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4981658458709717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4089752733707428},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.12562575936317444},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1709.09885","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.09885","pdf_url":"https://arxiv.org/pdf/1709.09885","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1709.09885","doi":"https://doi.org/10.48550/arxiv.1709.09885","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1709.09885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1709.09885","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1709.09885","pdf_url":"https://arxiv.org/pdf/1709.09885","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1484210532","https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1843891098","https://openalex.org/W1902237438","https://openalex.org/W1988939740","https://openalex.org/W2019759670","https://openalex.org/W2104246439","https://openalex.org/W2147527908","https://openalex.org/W2215376118","https://openalex.org/W2572908757","https://openalex.org/W2949300694","https://openalex.org/W2952566282","https://openalex.org/W2963069010","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2758288458","https://openalex.org/W2963874170","https://openalex.org/W2884611977","https://openalex.org/W2549624289","https://openalex.org/W7098640231","https://openalex.org/W3005912063","https://openalex.org/W2215041843","https://openalex.org/W1999957818","https://openalex.org/W2742267513","https://openalex.org/W2251040053"],"abstract_inverted_index":{"In":[0,53,70,100,189],"order":[1,190],"to":[2,13,27,34,122,165,191],"maximize":[3],"the":[4,17,29,35,45,123,128,131,143,147,150,157,174,177,182,185,193,198,216,224,231],"applicability":[5],"of":[6,21,184,204],"sentiment":[7,19,41,46,68,144],"analysis":[8,42],"results,":[9],"it":[10],"is":[11,57,105,114,134,153],"necessary":[12],"not":[14,220],"only":[15,43,221],"classify":[16,223],"overall":[18],"(positive/negative)":[20],"a":[22,75,88,95,108,117,169],"given":[23],"document":[24,50],"but":[25,227],"also":[26,228],"identify":[28,230],"main":[30],"words":[31,62,80,164,206,233],"that":[32,161,215],"contribute":[33],"classification.":[36,69],"However,":[37],"most":[38],"datasets":[39],"for":[40,48,77],"have":[44],"label":[47],"each":[49,103,112],"or":[51],"sentence.":[52,129],"other":[54],"words,":[55],"there":[56],"no":[58],"information":[59],"about":[60],"which":[61],"play":[63],"an":[64],"important":[65],"role":[66],"in":[67,127],"this":[71],"paper,":[72],"we":[73,155,196],"propose":[74],"method":[76,92],"identifying":[78],"key":[79],"discriminating":[81],"positive":[82],"and":[83,111,142,201],"negative":[84],"sentences":[85],"by":[86],"using":[87,136,173,207],"weakly":[89],"supervised":[90],"learning":[91],"based":[93],"on":[94],"convolutional":[96],"neural":[97],"network":[98],"(CNN).":[99],"our":[101],"model,":[102],"word":[104,124,158],"represented":[106,115],"as":[107,116,140,146],"continuous-valued":[109],"vector":[110,125],"sentence":[113,138,225],"matrix":[118],"whose":[119],"rows":[120],"correspond":[121],"used":[126],"Then,":[130],"CNN":[132,151,187],"model":[133,152,218],"trained":[135],"these":[137],"matrices":[139],"inputs":[141],"labels":[145],"output.":[148],"Once":[149],"trained,":[154],"implement":[156],"attention":[159],"mechanism":[160],"identifies":[162],"high-contributing":[163],"classification":[166,199],"results":[167],"with":[168,234],"class":[170],"activation":[171],"map,":[172],"weights":[175],"from":[176],"fully":[178],"connected":[179],"layer":[180],"at":[181],"end":[183],"learned":[186],"model.":[188],"verify":[192],"proposed":[194,217],"methodology,":[195],"evaluated":[197],"accuracy":[200],"inclusion":[202],"rate":[203],"polarity":[205,226,236],"two":[208],"movie":[209],"review":[210],"datasets.":[211],"Experimental":[212],"result":[213],"show":[214],"can":[219],"correctly":[222],"successfully":[229],"corresponding":[232],"high":[235],"scores.":[237]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2017-11-10T00:00:00"}