{"id":"https://openalex.org/W3094241099","doi":"https://doi.org/10.1109/icccnt49239.2020.9225665","title":"Deep Learning for Sentiment Analysis Based on Customer Reviews","display_name":"Deep Learning for Sentiment Analysis Based on Customer Reviews","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3094241099","doi":"https://doi.org/10.1109/icccnt49239.2020.9225665","mag":"3094241099"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt49239.2020.9225665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt49239.2020.9225665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","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/A5017593060","display_name":"B. Seetharamulu","orcid":"https://orcid.org/0000-0003-1395-691X"},"institutions":[{"id":"https://openalex.org/I4210138731","display_name":"ICFAI Foundation for Higher Education","ror":"https://ror.org/04p3pp808","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210138731"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"B. Seetharamulu","raw_affiliation_strings":["Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India","institution_ids":["https://openalex.org/I4210138731"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051118545","display_name":"B. Naresh Kumar Reddy","orcid":"https://orcid.org/0000-0001-8434-3673"},"institutions":[{"id":"https://openalex.org/I4210138731","display_name":"ICFAI Foundation for Higher Education","ror":"https://ror.org/04p3pp808","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210138731"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"B. Naresh Kumar Reddy","raw_affiliation_strings":["Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India","institution_ids":["https://openalex.org/I4210138731"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110243842","display_name":"K. Bramha Naidu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138731","display_name":"ICFAI Foundation for Higher Education","ror":"https://ror.org/04p3pp808","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210138731"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Bramha Naidu","raw_affiliation_strings":["Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India","institution_ids":["https://openalex.org/I4210138731"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017593060"],"corresponding_institution_ids":["https://openalex.org/I4210138731"],"apc_list":null,"apc_paid":null,"fwci":2.0551,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.89642314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T11644","display_name":"Spam and Phishing Detection","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.992900013923645,"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/computer-science","display_name":"Computer science","score":0.8132307529449463},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7811372876167297},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7411364316940308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7285789847373962},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6011843085289001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5182111859321594},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4494183659553528},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4425254464149475},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.41444075107574463}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8132307529449463},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7811372876167297},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7411364316940308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7285789847373962},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6011843085289001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5182111859321594},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4494183659553528},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4425254464149475},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.41444075107574463},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt49239.2020.9225665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt49239.2020.9225665","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1832693441","https://openalex.org/W1964613733","https://openalex.org/W2009086942","https://openalex.org/W2031998113","https://openalex.org/W2108646579","https://openalex.org/W2113459411","https://openalex.org/W2115023510","https://openalex.org/W2118585731","https://openalex.org/W2120615054","https://openalex.org/W2141631351","https://openalex.org/W2146502635","https://openalex.org/W2158899491","https://openalex.org/W2160660844","https://openalex.org/W2163922914","https://openalex.org/W2768787743","https://openalex.org/W2952230511","https://openalex.org/W2997308257","https://openalex.org/W3022228835","https://openalex.org/W4211186029","https://openalex.org/W4231109964","https://openalex.org/W4250860020","https://openalex.org/W6600949241","https://openalex.org/W6676984168","https://openalex.org/W6677656871","https://openalex.org/W6681435938","https://openalex.org/W6683738474"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2765903680","https://openalex.org/W4380075502","https://openalex.org/W4366307084"],"abstract_inverted_index":{"Online":[0],"reviews":[1,37],"became":[2],"popular":[3],"as":[4,46,115],"people":[5],"are":[6],"taking":[7],"decisions":[8],"with":[9,62],"the":[10,17,53,85,95,153,160,166,169],"help":[11],"of":[12,19,64,84,89,97,152,168],"them.":[13],"In":[14],"this":[15,20],"context,":[16],"purpose":[18],"project":[21],"is":[22,44,50,60,70,79,129,140,147,163],"to":[23,34,72,81,133,149],"develop":[24],"a":[25,58],"deep":[26,90,103,137],"learning":[27,55,91,104,138],"based":[28,51,125],"framework":[29,105,139],"that":[30,159],"can":[31],"be":[32],"used":[33,71],"classify":[35,73],"customer":[36],"into":[38],"positive":[39],"or":[40],"negative.":[41],"This":[42],"process":[43],"known":[45],"sentiment":[47,108],"analysis.":[48],"It":[49],"on":[52,94],"supervised":[54],"mechanisms":[56],"where":[57],"classifier":[59],"built":[61,80,148],"knowledge":[63],"training":[65,99],"data":[66],"and":[67,131,142],"then":[68],"it":[69],"testing":[74],"data.":[75,100],"A":[76,101,136,144],"prototype":[77,145],"application":[78,146],"demonstrate":[82,150],"proof":[83,151],"concept.":[86,154],"The":[87,155],"success":[88],"highly":[92],"relies":[93],"availability":[96],"large-scale":[98],"novel":[102],"for":[106],"review":[107],"classification":[109],"which":[110],"employs":[111],"prevalently":[112],"available":[113],"ratings":[114],"weak":[116],"supervision":[117],"signals.":[118],"An":[119],"algorithm":[120],"by":[121],"name":[122],"Deep":[123],"Learning":[124],"Sentiment":[126],"Analysis":[127],"(DLSA)":[128],"proposed":[130,141,161],"implemented":[132],"achieve":[134],"this.":[135],"implemented.":[143],"empirical":[156],"study":[157],"revealed":[158],"system":[162],"better":[164],"than":[165],"state":[167],"art.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}