{"id":"https://openalex.org/W4403748044","doi":"https://doi.org/10.48550/arxiv.2409.12840","title":"Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models","display_name":"Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models","publication_year":2024,"publication_date":"2024-09-19","ids":{"openalex":"https://openalex.org/W4403748044","doi":"https://doi.org/10.48550/arxiv.2409.12840"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2409.12840","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.12840","pdf_url":"https://arxiv.org/pdf/2409.12840","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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/2409.12840","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Raees, Muhammad","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I69716792","display_name":"Mirpur University of Science and Technology","ror":"https://ror.org/04qjkhc08","country_code":"PK","type":"education","lineage":["https://openalex.org/I69716792"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Raees, Muhammad","raw_affiliation_strings":["Mirpur University of Science and Technology , Mirpur , AJK , Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mirpur University of Science and Technology , Mirpur , AJK , Pakistan","institution_ids":["https://openalex.org/I69716792"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036255774","display_name":"Samina Fazilat","orcid":"https://orcid.org/0000-0002-9881-7136","observed_orcids":["https://orcid.org/0000-0002-9881-7136"]},"institutions":[{"id":"https://openalex.org/I69716792","display_name":"Mirpur University of Science and Technology","ror":"https://ror.org/04qjkhc08","country_code":"PK","type":"education","lineage":["https://openalex.org/I69716792"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Fazilat, Samina","raw_affiliation_strings":["Mirpur University of Science and Technology , Mirpur , AJK , Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mirpur University of Science and Technology , Mirpur , AJK , Pakistan","institution_ids":["https://openalex.org/I69716792"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I69716792"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","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/T10667","display_name":"Emotion and Mood Recognition","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"sentiment analysis","score":0.9150000214576721},{"id":"https://openalex.org/keywords/lexicon-based-sentiment-analysis","display_name":"lexicon-based sentiment analysis","score":0.8299999833106995},{"id":"https://openalex.org/keywords/text-polarity","display_name":"text polarity","score":0.8220000267028809},{"id":"https://openalex.org/keywords/subjectivity","display_name":"subjectivity","score":0.5590000152587891},{"id":"https://openalex.org/keywords/textblob","display_name":"TextBlob","score":0.9129999876022339},{"id":"https://openalex.org/keywords/vader","display_name":"VADER","score":0.8330000042915344},{"id":"https://openalex.org/keywords/twitter","display_name":"Twitter","score":0.4909999966621399},{"id":"https://openalex.org/keywords/tweets","display_name":"tweets","score":0.43799999356269836},{"id":"https://openalex.org/keywords/classification-model-comparison","display_name":"classification model comparison","score":0.625},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9430000185966492},{"id":"https://openalex.org/keywords/personality-judgment","display_name":"personality judgment","score":0.5450000166893005},{"id":"https://openalex.org/keywords/online-activities","display_name":"online activities","score":0.6039999723434448}],"concepts":[{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.8283426761627197},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6829959154129028},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6276376247406006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5502134561538696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5500426292419434},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3674291968345642},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.34578198194503784},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.06911510229110718}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2409.12840","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.12840","pdf_url":"https://arxiv.org/pdf/2409.12840","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2409.12840","doi":"https://doi.org/10.48550/arxiv.2409.12840","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.12840","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.12840","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.12840","pdf_url":"https://arxiv.org/pdf/2409.12840","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403748044.pdf","grobid_xml":"https://content.openalex.org/works/W4403748044.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2888662092","https://openalex.org/W3205826705","https://openalex.org/W2903394456","https://openalex.org/W2902285665","https://openalex.org/W2150818832","https://openalex.org/W2975174210","https://openalex.org/W2244029015","https://openalex.org/W2287843335","https://openalex.org/W1831473261","https://openalex.org/W4293870971"],"abstract_inverted_index":{"Sentiment":[0,11,113],"analysis":[1,12,34,122,138,179],"possesses":[2],"the":[3,14,18,23,50,62,70,74,116,127,131,134],"potential":[4],"of":[5,39,52,73,76,86,123,139,175],"diverse":[6],"applicability":[7],"on":[8,82,119,192],"digital":[9],"platforms.":[10],"extracts":[13],"polarity":[15,75],"to":[16,31,114],"understand":[17],"intensity":[19,51,132],"and":[20,35,54,68,111,130,153],"subjectivity":[21,55],"in":[22],"text.":[24,120,135],"This":[25,78],"work":[26,79],"uses":[27],"a":[28,66,83,183,188],"lexicon-based":[29,47,106],"method":[30],"perform":[32],"sentiment":[33,96,178],"shows":[36,125],"an":[37,172],"evaluation":[38],"classification":[40],"models":[41],"trained":[42],"over":[43],"textual":[44],"data.":[45],"The":[46,59,121,163],"methods":[48,107],"identify":[49],"emotion":[53],"at":[56],"word":[57,128],"levels.":[58],"categorization":[60],"identifies":[61],"informative":[63],"words":[64],"inside":[65],"text":[67,87],"specifies":[69],"quantitative":[71],"ranking":[72],"words.":[77],"is":[80,103,180],"based":[81,191],"multi-class":[84],"problem":[85],"being":[88],"labeled":[89],"as":[90],"positive,":[91],"negative,":[92],"or":[93],"neutral.":[94],"Twitter":[95,189],"dataset":[97],"containing":[98],"1.6":[99],"million":[100],"unprocessed":[101],"tweets":[102],"used":[104],"with":[105,171],"like":[108],"Text":[109],"Blob":[110],"Vader":[112],"introduce":[115],"neutrality":[117],"measure":[118],"lexicons":[124],"how":[126],"count":[129],"classify":[133],"A":[136],"comparative":[137],"machine":[140],"learning":[141],"models,":[142],"Naiive":[143],"Bayes,":[144],"Support":[145],"Vector":[146],"Machines,":[147],"Multinomial":[148],"Logistic":[149],"Regression,":[150],"Random":[151,169],"Forest,":[152],"Extreme":[154],"Gradient":[155],"(XG)":[156],"Boost":[157],"performed":[158],"across":[159],"multiple":[160],"performance":[161],"metrics.":[162],"best":[164],"estimations":[165],"are":[166],"achieved":[167],"through":[168],"Forest":[170],"accuracy":[173],"score":[174],"81%.":[176],"Additionally,":[177],"applied":[181],"for":[182],"personality":[184],"judgment":[185],"case":[186],"against":[187],"profile":[190],"online":[193],"activity.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2024-10-25T00:00:00"}