{"id":"https://openalex.org/W2585577269","doi":"https://doi.org/10.1109/bigdata.2016.7840818","title":"Prediction of Indian election using sentiment analysis on Hindi Twitter","display_name":"Prediction of Indian election using sentiment analysis on Hindi Twitter","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2585577269","doi":"https://doi.org/10.1109/bigdata.2016.7840818","mag":"2585577269"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5007604564","display_name":"Sharma Parul","orcid":null},"institutions":[{"id":"https://openalex.org/I51504820","display_name":"San Jose State University","ror":"https://ror.org/04qyvz380","country_code":"US","type":"education","lineage":["https://openalex.org/I51504820"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parul Sharma","raw_affiliation_strings":["Department of Computer Science, San Jose State University, San Jose, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, San Jose State University, San Jose, CA, USA","institution_ids":["https://openalex.org/I51504820"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019298277","display_name":"Teng-Sheng Moh","orcid":"https://orcid.org/0000-0002-2726-102X"},"institutions":[{"id":"https://openalex.org/I51504820","display_name":"San Jose State University","ror":"https://ror.org/04qyvz380","country_code":"US","type":"education","lineage":["https://openalex.org/I51504820"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Teng-Sheng Moh","raw_affiliation_strings":["Department of Computer Science, San Jose State University, San Jose, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, San Jose State University, San Jose, CA, USA","institution_ids":["https://openalex.org/I51504820"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51504820"],"apc_list":null,"apc_paid":null,"fwci":4.2708,"has_fulltext":false,"cited_by_count":149,"citation_normalized_percentile":{"value":0.96076676,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1966","last_page":"1971"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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":0.9998000264167786,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9487000107765198,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9419999718666077,"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":0.8135875463485718},{"id":"https://openalex.org/keywords/hindi","display_name":"Hindi","score":0.7924346923828125},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7831709384918213},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.704778790473938},{"id":"https://openalex.org/keywords/general-election","display_name":"General election","score":0.6972249150276184},{"id":"https://openalex.org/keywords/politics","display_name":"Politics","score":0.6407835483551025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6251934170722961},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6162315011024475},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4896693527698517},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48059388995170593},{"id":"https://openalex.org/keywords/bag-of-words-model","display_name":"Bag-of-words model","score":0.44412681460380554},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.39573997259140015},{"id":"https://openalex.org/keywords/law","display_name":"Law","score":0.1463955044746399}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8135875463485718},{"id":"https://openalex.org/C519982507","wikidata":"https://www.wikidata.org/wiki/Q1568","display_name":"Hindi","level":2,"score":0.7924346923828125},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7831709384918213},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.704778790473938},{"id":"https://openalex.org/C59742305","wikidata":"https://www.wikidata.org/wiki/Q1076105","display_name":"General election","level":3,"score":0.6972249150276184},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.6407835483551025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6251934170722961},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162315011024475},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4896693527698517},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48059388995170593},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.44412681460380554},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.39573997259140015},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.1463955044746399}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840818","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840818","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W40549020","https://openalex.org/W187430138","https://openalex.org/W746832161","https://openalex.org/W1650620342","https://openalex.org/W2029046727","https://openalex.org/W2148188454","https://openalex.org/W2165571577","https://openalex.org/W2171645516","https://openalex.org/W2187227891","https://openalex.org/W2244451798","https://openalex.org/W2250219546","https://openalex.org/W2250525668","https://openalex.org/W2251899558","https://openalex.org/W2252256452","https://openalex.org/W6601618085","https://openalex.org/W6607602984","https://openalex.org/W6621912290","https://openalex.org/W6636800069","https://openalex.org/W6684191474","https://openalex.org/W6686735548","https://openalex.org/W6691284211","https://openalex.org/W6691572672","https://openalex.org/W6691624895"],"related_works":["https://openalex.org/W3021501837","https://openalex.org/W3152580332","https://openalex.org/W4389581355","https://openalex.org/W2345668612","https://openalex.org/W2269995117","https://openalex.org/W1824099197","https://openalex.org/W2401076195","https://openalex.org/W1594281393","https://openalex.org/W4281766958","https://openalex.org/W2108601311"],"abstract_inverted_index":{"Sentiment":[0],"analysis":[1,149],"is":[2,16,42],"considered":[3,141],"to":[4,18,44,59,116,196],"be":[5],"a":[6,75,78,181],"category":[7],"of":[8,77,101,134,139,147,212,239],"machine":[9],"learning":[10],"and":[11,31,33,39,104,113,120,128,168],"natural":[12],"language":[13],"processing.":[14],"It":[15,41],"used":[17,55],"extricate,":[19],"recognize,":[20],"or":[21],"portray":[22],"opinions":[23],"from":[24,48],"different":[25,51],"content":[26],"structures,":[27],"including":[28],"news,":[29],"audits":[30],"articles":[32],"categorizes":[34],"them":[35],"as":[36,125,227],"positive,":[37,126],"neutral":[38],"negative.":[40],"difficult":[43],"predict":[45],"election":[46,194],"results":[47,146],"tweets":[49,61,72],"in":[50,62,86,96,191,202,215],"Indian":[52,142,176,232],"languages.":[53],"We":[54,65,98,107,130],"Twitter":[56,135],"Archiver":[57],"tool":[58],"get":[60],"Hindi":[63],"language.":[64],"performed":[66],"data":[67,124],"(text)":[68],"mining":[69],"on":[70],"42,235":[71],"collected":[73],"over":[74],"period":[76,91],"month":[79],"that":[80,184],"referenced":[81],"five":[82],"national":[83],"political":[84,143,225],"parties":[85],"India,":[87],"during":[88],"the":[89,122,132,140,148,154,163,170,175,185,192,197,216,228],"campaigning":[90],"for":[92,150,159,169],"general":[93,193,218],"state":[94],"elections":[95,190],"2016.":[97],"made":[99],"use":[100],"both":[102],"supervised":[103],"unsupervised":[105],"approaches.":[106],"utilized":[108],"Dictionary":[109,171],"Based,":[110],"Naive":[111,151],"Bayes":[112,152],"SVM":[114,160,179],"algorithm":[115],"build":[117],"our":[118],"classifier":[119],"classified":[121],"test":[123],"negative":[127],"neutral.":[129],"identified":[131],"sentiment":[133,199],"users":[136],"towards":[137],"each":[138],"parties.":[144],"The":[145],"was":[153,162,174],"BJP":[155,164,186,208],"(Bhartiya":[156,165],"Janta":[157,166],"Party),":[158],"it":[161,173,205],"Party)":[167],"Approach":[172],"Nathional":[177],"Congress.":[178],"predicted":[180],"78.4%":[182],"chance":[183],"would":[187],"win":[188],"more":[189,221],"due":[195],"positive":[198],"they":[200],"received":[201],"tweets.":[203],"As":[204],"turned":[206],"out,":[207],"won":[209,236],"60":[210],"out":[211,238],"126":[213,240],"constituencies":[214],"2016":[217],"election,":[219],"far":[220],"than":[222],"any":[223],"other":[224],"party":[226,230],"next":[229],"(the":[231],"National":[233],"Congress)":[234],"only":[235],"26":[237],"constituencies.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":28},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}