{"id":"https://openalex.org/W2807038642","doi":"https://doi.org/10.1145/3195106.3195111","title":"Sentiment Analysis Model on Weather Related Tweets with Deep Neural Network","display_name":"Sentiment Analysis Model on Weather Related Tweets with Deep Neural Network","publication_year":2018,"publication_date":"2018-02-26","ids":{"openalex":"https://openalex.org/W2807038642","doi":"https://doi.org/10.1145/3195106.3195111","mag":"2807038642"},"language":"en","primary_location":{"id":"doi:10.1145/3195106.3195111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3195106.3195111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 10th International Conference on Machine Learning and Computing","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/A5000318430","display_name":"Jun Qian","orcid":"https://orcid.org/0000-0001-7546-9506"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Qian","raw_affiliation_strings":["Beijing Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082774439","display_name":"Zhendong Niu","orcid":"https://orcid.org/0000-0002-0576-7572"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhendong Niu","raw_affiliation_strings":["Beijing Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003673476","display_name":"Chongyang Shi","orcid":"https://orcid.org/0000-0003-4905-8994"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongyang Shi","raw_affiliation_strings":["Beijing Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000318430"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":1.1402,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.83488801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9994000196456909,"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":0.9994000196456909,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9969000220298767,"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":0.98089998960495,"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":0.811299204826355},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7719528079032898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6896096467971802},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6387170553207397},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6176623702049255},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6057742238044739},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6026079654693604},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5797125101089478},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5779976844787598},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.5149232149124146},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49160465598106384},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4898047149181366},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4837896525859833},{"id":"https://openalex.org/keywords/bag-of-words-model","display_name":"Bag-of-words model","score":0.44751784205436707},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4289562702178955},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42672282457351685},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.42261260747909546},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41619938611984253},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09519723057746887}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.811299204826355},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7719528079032898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6896096467971802},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6387170553207397},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6176623702049255},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6057742238044739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6026079654693604},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5797125101089478},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5779976844787598},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.5149232149124146},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49160465598106384},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4898047149181366},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4837896525859833},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.44751784205436707},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4289562702178955},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42672282457351685},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.42261260747909546},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41619938611984253},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09519723057746887},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3195106.3195111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3195106.3195111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 10th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W40549020","https://openalex.org/W179875071","https://openalex.org/W1591801644","https://openalex.org/W1899794420","https://openalex.org/W1938755728","https://openalex.org/W2061039971","https://openalex.org/W2077587655","https://openalex.org/W2084046180","https://openalex.org/W2097726431","https://openalex.org/W2120615054","https://openalex.org/W2130942839","https://openalex.org/W2153579005","https://openalex.org/W2170240176","https://openalex.org/W2242874043","https://openalex.org/W2250966211","https://openalex.org/W2252215182","https://openalex.org/W2919115771","https://openalex.org/W2949888546","https://openalex.org/W2950133940","https://openalex.org/W2950577311","https://openalex.org/W2951559648","https://openalex.org/W2963012544","https://openalex.org/W2963921497","https://openalex.org/W4205184193","https://openalex.org/W6600757733","https://openalex.org/W6820111552"],"related_works":["https://openalex.org/W4220909832","https://openalex.org/W2760392765","https://openalex.org/W2566238543","https://openalex.org/W2773616286","https://openalex.org/W4200241356","https://openalex.org/W4385572842","https://openalex.org/W4307004515","https://openalex.org/W4385575011","https://openalex.org/W3080191145","https://openalex.org/W2805180890"],"abstract_inverted_index":{"Weather":[0],"related":[1],"tweets":[2],"are":[3,70],"user's":[4,136],"comments":[5],"about":[6,14],"daily":[7],"weather.":[8],"We":[9],"can":[10,60,94],"gain":[11],"useful":[12],"information":[13],"how":[15],"weather":[16,139],"influence":[17],"people's":[18],"mood":[19],"by":[20],"analyzing":[21,135],"them.":[22],"This":[23],"is":[24,51,131],"what":[25],"we":[26,82,104],"called":[27],"opinion":[28,36],"mining":[29,37],"in":[30,117],"natural":[31],"language":[32],"processing":[33],"field.":[34],"Traditional":[35],"algorithm":[38],"use":[39,112],"feature":[40,58],"engineering":[41],"to":[42,64,75],"build":[43],"sentence":[44],"model,":[45],"and":[46,68,111],"classifier":[47],"like":[48],"naive":[49],"bayes":[50],"used":[52],"for":[53,100],"further":[54],"classification.":[55],"However,":[56],"these":[57],"vectors":[59],"sometimes":[61],"be":[62],"insufficient":[63],"represent":[65],"the":[66,76,123],"text,":[67],"they":[69],"manually":[71],"designed,":[72],"highly":[73],"relevant":[74],"problem's":[77],"background.":[78],"In":[79],"this":[80],"work1,":[81],"propose":[83],"a":[84],"method":[85,130],"modeling":[86],"text":[87,97],"based":[88],"on":[89,138],"deep":[90],"learning":[91],"approach,":[92],"which":[93],"automatically":[95],"extract":[96],"feature.":[98],"As":[99],"word's":[101,109],"vector":[102],"representation,":[103,110],"incorporate":[105],"linguistic":[106],"knowledge":[107],"into":[108],"three":[113],"different":[114],"word":[115],"representations":[116],"our":[118,129],"model.":[119],"The":[120],"performance":[121],"of":[122],"sentiment":[124,137],"analysis":[125],"system":[126],"shows":[127],"that":[128],"an":[132],"efficient":[133],"way":[134],"events.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2025-12-06T23:10:59.065948","created_date":"2025-10-10T00:00:00"}