Publicação
Dynamic topic modeling using social network analytics
| Resumo: | Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters’ structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters. |
|---|---|
| Autores principais: | Tabassum, Shazia |
| Outros Autores: | Gama, João; Azevedo, Paulo J.; Teixeira, Luis; Martins, Carlos; Martins, Andre |
| Assunto: | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1867439075457236992 |
|---|---|
| author | Tabassum, Shazia |
| author2 | Gama, João Azevedo, Paulo J. Teixeira, Luis Martins, Carlos Martins, Andre |
| author2_role | author author author author author |
| author_facet | Tabassum, Shazia Gama, João Azevedo, Paulo J. Teixeira, Luis Martins, Carlos Martins, Andre |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Tabassum, Shazia\"},{\"Person.name\":\"Gama, João\"},{\"Person.name\":\"Azevedo, Paulo J.\"},{\"Person.name\":\"Teixeira, Luis\"},{\"Person.name\":\"Martins, Carlos\"},{\"Person.name\":\"Martins, Andre\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Tabassum, Shazia Gama, João Azevedo, Paulo J. Teixeira, Luis Martins, Carlos Martins, Andre |
| datacite.date.Accepted.fl_str_mv | 2021-09-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-03-31T12:32:17Z |
| datacite.date.embargoed.fl_str_mv | 2024-03-31T12:32:17Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| datacite.titles.title.fl_str_mv | Dynamic topic modeling using social network analytics |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Tabassum, Shazia Gama, João Azevedo, Paulo J. Teixeira, Luis Martins, Carlos Martins, Andre |
| dc.date.Accepted.fl_str_mv | 2021-09-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-03-31T12:32:17Z |
| dc.date.embargoed.fl_str_mv | 2024-03-31T12:32:17Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/90304 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer, Cham |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| dc.title.fl_str_mv | Dynamic topic modeling using social network analytics |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters’ structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/444e3394-ae89-41ca-8406-ec46e38cb987/download |
| id | rum_d0349724173ab8eb151fb7ab2de6cbd2 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/90304 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/90304 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Tabassum, Shazia Gama, João Azevedo, Paulo J. Teixeira, Luis Martins, Carlos Martins, Andre |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Springer, Cham |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engSpringer, ChamporTopic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters’ structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters.application/pdfporDynamic topic modeling using social network analyticsTabassum, ShaziaGama, JoãoAzevedo, Paulo J.Teixeira, LuisMartins, CarlosMartins, AndreHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptCITATIONTabassum, S., Gama, J., Azevedo, P., Teixeira, L., Martins, C., Martins, A. (2021). Dynamic Topic Modeling Using Social Network Analytics. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_39ISBNIsPartOf978-3-030-86229-9DOIIsPartOf10.1007/978-3-030-86230-5_39EISBNIsPartOf978-3-030-86230-52024-03-31T12:32:17Z2021-092021-09-01T00:00:00ZHandlehttps://hdl.handle.net/1822/90304http://purl.org/coar/access_right/c_abf2open accesshttp://www.oecd.org/science/inno/38235147.pdfFields of Science and Technology (FOS)Ciências Naturais::Ciências da Computação e da Informaçãohttps://sdgs.un.org/goalsSustainable Development Goals (SDG)Indústria, inovação e infraestruturas1417502 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2021-09http://creativecommons.org/licenses/by-nc-sa/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/444e3394-ae89-41ca-8406-ec46e38cb987/download |
| spellingShingle | Dynamic topic modeling using social network analytics Tabassum, Shazia Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| status | SINGLETON |
| subject.other.fl_str_mv | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| title | Dynamic topic modeling using social network analytics |
| title_full | Dynamic topic modeling using social network analytics |
| title_fullStr | Dynamic topic modeling using social network analytics |
| title_full_unstemmed | Dynamic topic modeling using social network analytics |
| title_short | Dynamic topic modeling using social network analytics |
| title_sort | Dynamic topic modeling using social network analytics |
| topic | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| topic_facet | Ciências Naturais::Ciências da Computação e da Informação Indústria, inovação e infraestruturas |
| url | https://hdl.handle.net/1822/90304 |
| visible | 1 |