Public opinion mining using natural language processing technique for improvisation towards smart city.
Identifieur interne : 000093 ( Main/Exploration ); précédent : 000092; suivant : 000094Public opinion mining using natural language processing technique for improvisation towards smart city.
Auteurs : S. Leelavathy [Inde] ; M. Nithya [Inde]Source :
- International journal of speech technology [ 1381-2416 ] ; 2020.
Abstract
In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information's, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.
DOI: 10.1007/s10772-020-09766-z
PubMed: 33199973
PubMed Central: PMC7656096
Affiliations:
Links toward previous steps (curation, corpus...)
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Nithya</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Missions Research Foundation (Deemed to be University), Salem, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Missions Research Foundation (Deemed to be University), Salem</wicri:regionArea><wicri:noRegion>Salem</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:33199973</idno><idno type="pmid">33199973</idno><idno type="doi">10.1007/s10772-020-09766-z</idno><idno type="pmc">PMC7656096</idno><idno type="wicri:Area/Main/Corpus">000038</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000038</idno><idno type="wicri:Area/Main/Curation">000038</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000038</idno><idno type="wicri:Area/Main/Exploration">000038</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Public opinion mining using natural language processing technique for improvisation towards smart city.</title><author><name sortKey="Leelavathy, S" sort="Leelavathy, S" uniqKey="Leelavathy S" first="S" last="Leelavathy">S. Leelavathy</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (Deemed to be University), Paiyanoor, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (Deemed to be University), Paiyanoor</wicri:regionArea><wicri:noRegion>Paiyanoor</wicri:noRegion></affiliation></author><author><name sortKey="Nithya, M" sort="Nithya, M" uniqKey="Nithya M" first="M" last="Nithya">M. Nithya</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Missions Research Foundation (Deemed to be University), Salem, India.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Missions Research Foundation (Deemed to be University), Salem</wicri:regionArea><wicri:noRegion>Salem</wicri:noRegion></affiliation></author></analytic><series><title level="j">International journal of speech technology</title><idno type="ISSN">1381-2416</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information's, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33199973</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>18</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">1381-2416</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Nov</Month><Day>11</Day></PubDate></JournalIssue><Title>International journal of speech technology</Title><ISOAbbreviation>Int J Speech Technol</ISOAbbreviation></Journal><ArticleTitle>Public opinion mining using natural language processing technique for improvisation towards smart city.</ArticleTitle><Pagination><MedlinePgn>1-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s10772-020-09766-z</ELocationID><Abstract><AbstractText>In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information's, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.</AbstractText><CopyrightInformation>© Springer Science+Business Media, LLC, part of Springer Nature 2020.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Leelavathy</LastName><ForeName>S</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0002-4235-8268</Identifier><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions Research Foundation (Deemed to be University), Paiyanoor, India.</Affiliation><Identifier Source="GRID">grid.444708.b</Identifier><Identifier Source="ISNI">0000 0004 1799 6895</Identifier></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nithya</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Computer Science and Engineering, Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Missions Research Foundation (Deemed to be University), Salem, India.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Int J Speech Technol</MedlineTA><NlmUniqueID>9888855</NlmUniqueID><ISSNLinking>1381-2416</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Covid 19</Keyword><Keyword MajorTopicYN="N">Fuzzy logic</Keyword><Keyword MajorTopicYN="N">Hidden Markov models</Keyword><Keyword MajorTopicYN="N">Natural language processing</Keyword><Keyword MajorTopicYN="N">Speech processing</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>10</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>17</Day><Hour>6</Hour><Minute>22</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>11</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>11</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33199973</ArticleId><ArticleId IdType="doi">10.1007/s10772-020-09766-z</ArticleId><ArticleId IdType="pii">9766</ArticleId><ArticleId IdType="pmc">PMC7656096</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
<ReferenceList><Reference><Citation>Transl Psychiatry. 2016 Jul 19;6:e856</Citation><ArticleIdList><ArticleId IdType="pubmed">27434490</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2017 May 26;12(5):e0177926</Citation><ArticleIdList><ArticleId IdType="pubmed">28552969</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2017 Aug 9;12(8):e0182428</Citation><ArticleIdList><ArticleId IdType="pubmed">28792979</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Leelavathy, S" sort="Leelavathy, S" uniqKey="Leelavathy S" first="S" last="Leelavathy">S. Leelavathy</name></noRegion><name sortKey="Nithya, M" sort="Nithya, M" uniqKey="Nithya M" first="M" last="Nithya">M. Nithya</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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