{"id":"https://openalex.org/W7118178724","doi":"https://doi.org/10.1109/access.2025.3645352","title":"Robust Sentiment and Semantic Analysis of Small and Medium-Sized News Headline Datasets: A Study on Sports, Science, and Agricultural Domains","display_name":"Robust Sentiment and Semantic Analysis of Small and Medium-Sized News Headline Datasets: A Study on Sports, Science, and Agricultural Domains","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7118178724","doi":"https://doi.org/10.1109/access.2025.3645352"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3645352","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645352","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3645352","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077978308","display_name":"Zijun Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Zijun Liang","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"],"raw_orcid":"https://orcid.org/0009-0006-2879-5521","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113663428","display_name":"P. Y. Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Pengyu Lai","raw_affiliation_strings":["Institution of Collaborative Innovation, University of Macau, Macau, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institution of Collaborative Innovation, University of Macau, Macau, China","institution_ids":["https://openalex.org/I204512498"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108313947","display_name":"Guanpeng Su","orcid":null},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Guanpeng Su","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109682433","display_name":"Rouying Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Rouying Wu","raw_affiliation_strings":["Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China","institution_ids":["https://openalex.org/I111950717"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121919055","display_name":"Su Kit Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Su-Kit Tang","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"],"raw_orcid":"https://orcid.org/0000-0001-8104-7887","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","institution_ids":["https://openalex.org/I49835588"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059002802","display_name":"Dennis Wong","orcid":"https://orcid.org/0000-0002-6242-164X"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Dennis Wong","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macau, China"],"raw_orcid":"https://orcid.org/0000-0002-6242-164X","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","institution_ids":["https://openalex.org/I49835588"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02901087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"3852","last_page":"3896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.802299976348877,"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":0.802299976348877,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.04149999842047691,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.013299999758601189,"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/headline","display_name":"Headline","score":0.8159000277519226},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7121999859809875},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5641999840736389},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5235999822616577},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5026000142097473},{"id":"https://openalex.org/keywords/thematic-map","display_name":"Thematic map","score":0.47540000081062317},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3783999979496002},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.3693000078201294}],"concepts":[{"id":"https://openalex.org/C2778689934","wikidata":"https://www.wikidata.org/wiki/Q1313396","display_name":"Headline","level":2,"score":0.8159000277519226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8134999871253967},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7121999859809875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6858000159263611},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5641999840736389},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5235999822616577},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5083000063896179},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5026000142097473},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47290000319480896},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C2778109090","wikidata":"https://www.wikidata.org/wiki/Q7781195","display_name":"Thematic structure","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C74196892","wikidata":"https://www.wikidata.org/wiki/Q7781188","display_name":"Thematic analysis","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3645352","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645352","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3645352","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645352","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6060054302215576,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2908092214","https://openalex.org/W3084074548","https://openalex.org/W3103897629","https://openalex.org/W3105966348","https://openalex.org/W3128897032","https://openalex.org/W3139323134","https://openalex.org/W3141537781","https://openalex.org/W3157976194","https://openalex.org/W3161176628","https://openalex.org/W3163841364","https://openalex.org/W3189209532","https://openalex.org/W3190492058","https://openalex.org/W3191198889","https://openalex.org/W3200532577","https://openalex.org/W3205895757","https://openalex.org/W4207030529","https://openalex.org/W4210827551","https://openalex.org/W4220681974","https://openalex.org/W4282006100","https://openalex.org/W4309920805","https://openalex.org/W4317388187","https://openalex.org/W4320497301","https://openalex.org/W4362558246","https://openalex.org/W4376126986","https://openalex.org/W4382246105","https://openalex.org/W4385151530","https://openalex.org/W4385245566","https://openalex.org/W4385407692","https://openalex.org/W4386066299","https://openalex.org/W4387233876","https://openalex.org/W4388981152","https://openalex.org/W4389001310","https://openalex.org/W4389485693","https://openalex.org/W4389981801","https://openalex.org/W4393405326","https://openalex.org/W4396827130","https://openalex.org/W4401114395","https://openalex.org/W4401495975","https://openalex.org/W4409744609","https://openalex.org/W4409800868"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"the":[3,134,167,184],"application":[4,46],"of":[5,187],"deep":[6,113],"learning":[7,11,96,114],"and":[8,16,21,28,84,106,111,127,141,160,179,198],"advanced":[9],"machine":[10,95],"techniques":[12],"to":[13,39,47,80],"sentiment":[14,178],"analysis":[15,27],"thematic":[17,173],"clustering":[18],"in":[19],"small":[20],"medium-sized":[22],"news":[23,50],"datasets.":[24,51],"Sentiment":[25],"semantic":[26,65],"scoring":[29],"were":[30,57],"initially":[31],"performed":[32],"using":[33],"GPT-4,":[34],"customized":[35],"with":[36,70,108],"domain-specific":[37,49,193],"prompts":[38],"capture":[40],"nuanced":[41,177],"terminology,":[42],"representing":[43],"a":[44,67,71],"novel":[45],"small,":[48,192],"Based":[52],"on":[53],"these":[54],"scores,":[55],"data":[56],"labeled":[58],"as":[59],"positive,":[60],"negative,":[61],"or":[62],"neutral.":[63],"For":[64],"classification,":[66],"TF-IDF-SVM-OvR":[68],"model":[69,162],"linear":[72,130],"kernel":[73],"was":[74,90,170],"developed,":[75],"incorporating":[76],"feature":[77],"engineering":[78],"tailored":[79],"low-resource,":[81],"small-domain":[82],"datasets":[83,123],"imbalance-aware":[85],"OvR":[86],"classification.":[87],"Its":[88],"performance":[89],"compared":[91],"against":[92],"six":[93],"traditional":[94],"models":[97,115],"(Random":[98],"Forest,":[99],"GBM,":[100],"KNN,":[101],"Logistic":[102],"Regression,":[103],"Naive":[104],"Bayes,":[105],"SVM-OvR":[107,131],"RBF":[109],"kernel)":[110],"five":[112],"(TextCNN,":[116],"LSTM-CNN,":[117],"MobileNet,":[118],"TinyBERT,":[119],"OfficialSimpleTransformer)":[120],"across":[121],"four":[122],"(Sports,":[124],"Science,":[125],"Agriculture,":[126],"Mixed).":[128],"The":[129],"consistently":[132],"achieved":[133],"highest":[135],"test":[136],"accuracies":[137],"(81.8":[138],"\u2013":[139,145],"87.1%)":[140],"F1":[142],"scores":[143],"(81.2":[144],"85.5%),":[146],"significantly":[147],"outperforming":[148],"baselines":[149],"(p":[150],"<":[151],"0.05),":[152],"while":[153],"maintaining":[154],"moderate":[155],"training":[156],"times":[157],"(6.9\u201375.6":[158],"s)":[159],"low":[161],"sizes":[163],"(0.47\u20131.44":[164],"MB).":[165],"Additionally,":[166],"Qwen2-Birch":[168],"combination":[169],"employed":[171],"for":[172,191],"clustering,":[174],"effectively":[175],"capturing":[176],"topics.":[180],"These":[181],"results":[182],"highlight":[183],"practical":[185],"value":[186],"this":[188],"integrated":[189],"approach":[190],"datasets,":[194],"emphasizing":[195],"robustness,":[196],"efficiency,":[197],"reproducibility.":[199]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-05T00:00:00"}