{"id":"https://openalex.org/W2594108779","doi":"https://doi.org/10.3390/a10010034","title":"A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek","display_name":"A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek","publication_year":2017,"publication_date":"2017-03-06","ids":{"openalex":"https://openalex.org/W2594108779","doi":"https://doi.org/10.3390/a10010034","mag":"2594108779"},"language":"en","primary_location":{"id":"doi:10.3390/a10010034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a10010034","pdf_url":"https://www.mdpi.com/1999-4893/10/1/34/pdf?version=1488969678","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/10/1/34/pdf?version=1488969678","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070942091","display_name":"Vasileios Athanasiou","orcid":"https://orcid.org/0009-0005-5731-5411"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Vasileios Athanasiou","raw_affiliation_strings":["Artificial Intelligence Laboratory, University of the Aegean, 2 Palama Street, 83200 Samos, Greece"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Laboratory, University of the Aegean, 2 Palama Street, 83200 Samos, Greece","institution_ids":["https://openalex.org/I98805295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012359011","display_name":"Manolis \u039caragoudakis","orcid":"https://orcid.org/0000-0001-7701-0141"},"institutions":[{"id":"https://openalex.org/I98805295","display_name":"University of the Aegean","ror":"https://ror.org/03zsp3p94","country_code":"GR","type":"education","lineage":["https://openalex.org/I98805295"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Manolis Maragoudakis","raw_affiliation_strings":["Artificial Intelligence Laboratory, University of the Aegean, 2 Palama Street, 83200 Samos, Greece"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Laboratory, University of the Aegean, 2 Palama Street, 83200 Samos, Greece","institution_ids":["https://openalex.org/I98805295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012359011"],"corresponding_institution_ids":["https://openalex.org/I98805295"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.7302,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.92242479,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"10","issue":"1","first_page":"34","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9984999895095825,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9929999709129333,"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":0.835884153842926},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6839886903762817},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6626158952713013},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6428049802780151},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6405596733093262},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6292536854743958},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6008769869804382},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5153629183769226},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.5060941576957703},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.49872684478759766},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44352155923843384},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32823455333709717},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.12122467160224915}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.835884153842926},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6839886903762817},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6626158952713013},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6428049802780151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6405596733093262},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6292536854743958},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6008769869804382},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5153629183769226},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.5060941576957703},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.49872684478759766},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44352155923843384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32823455333709717},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.12122467160224915},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/a10010034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a10010034","pdf_url":"https://www.mdpi.com/1999-4893/10/1/34/pdf?version=1488969678","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a98c2d057bef4e5f881c638b957bb53a","is_oa":true,"landing_page_url":"https://doaj.org/article/a98c2d057bef4e5f881c638b957bb53a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 10, Iss 1, p 34 (2017)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1999-4893/10/1/34/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/a10010034","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms; Volume 10; Issue 1; Pages: 34","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/a10010034","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a10010034","pdf_url":"https://www.mdpi.com/1999-4893/10/1/34/pdf?version=1488969678","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2594108779.pdf","grobid_xml":"https://content.openalex.org/works/W2594108779.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W66373487","https://openalex.org/W103965747","https://openalex.org/W155754185","https://openalex.org/W169052826","https://openalex.org/W195302895","https://openalex.org/W1549559390","https://openalex.org/W1573149547","https://openalex.org/W1574454071","https://openalex.org/W1634225020","https://openalex.org/W1728658630","https://openalex.org/W1815764283","https://openalex.org/W1847329061","https://openalex.org/W1945122192","https://openalex.org/W2017489100","https://openalex.org/W2018739138","https://openalex.org/W2023460117","https://openalex.org/W2034090215","https://openalex.org/W2040233874","https://openalex.org/W2059503205","https://openalex.org/W2060238174","https://openalex.org/W2084046180","https://openalex.org/W2088794999","https://openalex.org/W2097726431","https://openalex.org/W2104514557","https://openalex.org/W2119759918","https://openalex.org/W2122585011","https://openalex.org/W2123753389","https://openalex.org/W2139575250","https://openalex.org/W2142262074","https://openalex.org/W2146111747","https://openalex.org/W2146867136","https://openalex.org/W2148143831","https://openalex.org/W2163259319","https://openalex.org/W2250333314","https://openalex.org/W2251550922","https://openalex.org/W2261339705","https://openalex.org/W2274912527","https://openalex.org/W2301116560","https://openalex.org/W2419372661","https://openalex.org/W2510370086","https://openalex.org/W2756474357","https://openalex.org/W2949965121","https://openalex.org/W3027304069","https://openalex.org/W4205184193","https://openalex.org/W4231109964","https://openalex.org/W4250498219","https://openalex.org/W4255173720","https://openalex.org/W6606274072","https://openalex.org/W6606879723","https://openalex.org/W6638928509","https://openalex.org/W6656152552","https://openalex.org/W6672681305","https://openalex.org/W6677771139","https://openalex.org/W6694131070"],"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/W2766514146","https://openalex.org/W2474028989","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W4310224730"],"abstract_inverted_index":{"Sentiment":[0],"analysis":[1,114,137],"has":[2,30,180],"played":[3],"a":[4,78,135,154,165,241,288,330,335,395],"primary":[5],"role":[6],"in":[7,23,84,116,297,307,403],"text":[8,366],"classification.":[9],"It":[10,42],"is":[11,43,122,147,176,218,231],"an":[12,259],"undoubted":[13],"fact":[14],"that":[15,177,245,262,333,375],"some":[16],"years":[17],"ago,":[18],"textual":[19,54],"information":[20,29,97],"was":[21,327],"spreading":[22],"manageable":[24],"rates;":[25],"however,":[26],"nowadays,":[27],"such":[28,353],"overcome":[31],"even":[32],"the":[33,50,61,88,141,144,159,182,207,210,213,234,270,281,293,320,339,344,371,376],"most":[34],"ambiguous":[35],"expectations":[36],"and":[37,73,102,187,222,342,368,387,390,407],"constantly":[38],"grows":[39],"within":[40],"seconds.":[41],"therefore":[44],"quite":[45,191],"complex":[46],"to":[47,86,94,143,149,199,272],"cope":[48,383],"with":[49,170,233,265,275,287,384],"vast":[51],"amount":[52],"of":[53,90,126,138,162,184,209,215,228,237,295,303,337,356,360,363,370,397,405],"data":[55],"particularly":[56],"if":[57],"we":[58,195,254,285],"also":[59,190],"take":[60],"incremental":[62],"production":[63],"speed":[64],"into":[65,203],"account.":[66],"Social":[67],"media,":[68],"e-commerce,":[69],"news":[70],"articles,":[71],"comments":[72,318],"opinions":[74],"are":[75,189],"broadcasted":[76],"on":[77,112,158],"daily":[79],"basis.":[80],"A":[81],"rational":[82],"solution,":[83],"order":[85],"handle":[87],"abundance":[89],"data,":[91],"would":[92],"be":[93],"build":[95],"automated":[96],"processing":[98,129],"systems,":[99],"for":[100,131,168,249,280],"analyzing":[101],"extracting":[103],"meaningful":[104],"patterns":[105],"from":[106,140],"text.":[107],"The":[108,173,224,323],"present":[109],"paper":[110,152],"focuses":[111],"sentiment":[113],"applied":[115],"Greek":[117,201,365],"texts.":[118],"Thus":[119],"far,":[120],"there":[121,188],"no":[123],"wide":[124],"availability":[125],"natural":[127],"language":[128],"tools":[130,186],"Modern":[132],"Greek.":[133],"Hence,":[134],"thorough":[136],"Greek,":[139,238],"lexical":[142],"syntactical":[145],"level,":[146],"difficult":[148],"perform.":[150],"This":[151],"attempts":[153],"different":[155,266,351],"approach,":[156],"based":[157],"proven":[160],"capabilities":[161],"gradient":[163,256,378],"boosting,":[164],"well-known":[166],"technique":[167,332],"dealing":[169],"high-dimensional":[171,386],"data.":[172,278],"main":[174],"rationale":[175],"since":[178,212,292],"English":[179,229],"dominated":[181],"area":[183],"preprocessing":[185,367],"reliable":[192,221],"translation":[193,214,355,359,372],"services,":[194],"could":[196],"exploit":[197],"them":[198],"transform":[200],"tokens":[202,230,357],"English,":[204],"thus":[205],"assuring":[206],"precision":[208,406],"translation,":[211],"large":[216],"texts":[217],"not":[219],"always":[220],"meaningful.":[223],"new":[225],"feature":[226],"set":[227,236],"augmented":[232],"original":[235],"consequently":[239],"producing":[240],"high":[242,276],"dimensional":[243,277],"dataset":[244],"poses":[246],"certain":[247],"difficulties":[248],"any":[250],"traditional":[251,398],"classifier.":[252],"Accordingly,":[253],"apply":[255],"boosting":[257,379],"machines,":[258],"ensemble":[260],"algorithm":[261],"can":[263,381],"learn":[264],"loss":[267],"functions":[268],"providing":[269],"ability":[271],"work":[273],"efficiently":[274],"Moreover,":[279],"task":[282],"at":[283],"hand,":[284],"deal":[286],"class":[289,324,341],"imbalance":[290,325],"issues":[291,302],"distribution":[294],"sentiments":[296],"real-world":[298],"applications":[299],"often":[300],"displays":[301],"inequality.":[304],"For":[305],"example,":[306],"political":[308],"forums":[309],"or":[310,315],"electronic":[311],"discussions":[312],"about":[313],"immigration":[314],"religion,":[316],"negative":[317],"overwhelm":[319],"positive":[321],"ones.":[322],"problem":[326],"confronted":[328],"using":[329],"hybrid":[331],"performs":[334,391],"variation":[336],"under-sampling":[338],"majority":[340],"over-sampling":[343],"minority":[345],"class,":[346],"respectively.":[347],"Experimental":[348],"results,":[349],"considering":[350],"settings,":[352],"as":[354],"against":[358],"sentences,":[361],"consideration":[362],"limited":[364],"omission":[369],"phase,":[373],"demonstrated":[374],"proposed":[377],"framework":[380],"effectively":[382],"both":[385],"imbalanced":[388],"datasets":[389],"significantly":[392],"better":[393],"than":[394],"plethora":[396],"machine":[399],"learning":[400],"classification":[401],"approaches":[402],"terms":[404],"recall":[408],"measures.":[409]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3}],"updated_date":"2026-02-25T23:00:34.991745","created_date":"2025-10-10T00:00:00"}
