{"id":"https://openalex.org/W2984603541","doi":"https://doi.org/10.1145/3357384.3358136","title":"Modeling Sentiment Evolution for Social Incidents","display_name":"Modeling Sentiment Evolution for Social Incidents","publication_year":2019,"publication_date":"2019-11-03","ids":{"openalex":"https://openalex.org/W2984603541","doi":"https://doi.org/10.1145/3357384.3358136","mag":"2984603541"},"language":"en","primary_location":{"id":"doi:10.1145/3357384.3358136","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3358136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","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/A5036771793","display_name":"Yunjie Wang","orcid":"https://orcid.org/0000-0002-3080-5518"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunjie Wang","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423840","display_name":"Hui Li","orcid":"https://orcid.org/0000-0001-9139-3855"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Li","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100707569","display_name":"Chen Lin","orcid":"https://orcid.org/0000-0002-2275-997X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Lin","raw_affiliation_strings":["Xiamen University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036771793"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.5601,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75681884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2413","last_page":"2416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9998999834060669,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9987999796867371,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.9043644666671753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8038116693496704},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7713029384613037},{"id":"https://openalex.org/keywords/microblogging","display_name":"Microblogging","score":0.7288694977760315},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.7051021456718445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47800198197364807},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4399362802505493},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37292081117630005},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.33899328112602234},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32325512170791626},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1062752902507782}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.9043644666671753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8038116693496704},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7713029384613037},{"id":"https://openalex.org/C143275388","wikidata":"https://www.wikidata.org/wiki/Q92438","display_name":"Microblogging","level":3,"score":0.7288694977760315},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.7051021456718445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47800198197364807},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4399362802505493},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37292081117630005},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33899328112602234},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32325512170791626},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1062752902507782}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3357384.3358136","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3357384.3358136","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","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":16,"referenced_works":["https://openalex.org/W359818833","https://openalex.org/W2046902062","https://openalex.org/W2120340025","https://openalex.org/W2137630814","https://openalex.org/W2143376306","https://openalex.org/W2204494210","https://openalex.org/W2265846598","https://openalex.org/W2535901130","https://openalex.org/W2733078680","https://openalex.org/W2757014168","https://openalex.org/W2767841318","https://openalex.org/W2803646846","https://openalex.org/W2963282319","https://openalex.org/W4239946314","https://openalex.org/W4293052541","https://openalex.org/W4302443270"],"related_works":["https://openalex.org/W2728430307","https://openalex.org/W2107786128","https://openalex.org/W2053241453","https://openalex.org/W2153980712","https://openalex.org/W2537388533","https://openalex.org/W2978974359","https://openalex.org/W2021183651","https://openalex.org/W2036556872","https://openalex.org/W2017590198","https://openalex.org/W2353191283"],"abstract_inverted_index":{"Modeling":[0],"sentiment":[1,21,32,36,41,91,108,120,140],"evolution":[2,60,102,141],"for":[3,12,142],"social":[4,143],"incidents":[5],"in":[6,51,73],"Microblogs":[7],"is":[8,42,66,71],"of":[9,30,61,103,119,138],"vital":[10],"importance":[11],"both":[13],"enterprises":[14],"and":[15,34,106],"government":[16],"officials.":[17],"Existing":[18],"works":[19],"on":[20,94,115,123,135],"tracking":[22],"are":[23],"not":[24],"satisfying,":[25],"due":[26],"to":[27,68,86,98],"the":[28,59,62,74,83,101,107,116,136],"lack":[29],"entity-level":[31,40,90],"extraction":[33],"accurate":[35],"shift":[37,69,109],"detection.":[38],"Identifying":[39],"challenging":[43],"as":[44],"Microbloggers":[45],"often":[46],"use":[47],"multiple":[48],"opinion":[49,105],"expressions":[50],"a":[52,111],"sentence":[53],"which":[54,65],"targets":[55],"different":[56],"entities.":[57],"Moreover,":[58],"background":[63,104],"sentiment,":[64],"essential":[67],"detection,":[70],"ignored":[72],"previous":[75],"study.":[76],"To":[77],"address":[78],"these":[79],"issues,":[80],"we":[81,96],"leverage":[82],"proximity":[84],"information":[85],"obtain":[87],"more":[88],"precise":[89],"extraction.":[92],"Based":[93],"it,":[95],"propose":[97],"simultaneously":[99],"model":[100,114],"using":[110],"state":[112],"space":[113],"time":[117],"series":[118],"polarities.":[121],"Experiments":[122],"real":[124],"data":[125],"sets":[126],"demonstrate":[127],"that":[128],"our":[129],"proposed":[130],"approaches":[131],"outperform":[132],"state-of-the-art":[133],"methods":[134],"task":[137],"modeling":[139],"incidents.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}