Entity Profiling to Identify Actor Involvement in Topics of Social Media Content
The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will imp...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Gadjah Mada
2020-10-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/59869 |
| _version_ | 1818319471675179008 |
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| author | Puji Winar Cahyo Muhammad Habibi |
| author_facet | Puji Winar Cahyo Muhammad Habibi |
| author_sort | Puji Winar Cahyo |
| collection | DOAJ |
| description | The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news. |
| first_indexed | 2024-12-13T10:09:39Z |
| format | Article |
| id | doaj.art-9cce997d5985476aba9f1348d9caec22 |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-13T10:09:39Z |
| publishDate | 2020-10-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-9cce997d5985476aba9f1348d9caec222022-12-21T23:51:29ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582020-10-0114441742810.22146/ijccs.5986928589Entity Profiling to Identify Actor Involvement in Topics of Social Media ContentPuji Winar Cahyo0Muhammad Habibi1Department of Informatics, FTTI UNJANI, YogyakartaDepartment of Informatics, FTTI UNJANI, YogyakartaThe efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.https://jurnal.ugm.ac.id/ijccs/article/view/59869entity profilingtopic modelingsentiment analysisldasvm |
| spellingShingle | Puji Winar Cahyo Muhammad Habibi Entity Profiling to Identify Actor Involvement in Topics of Social Media Content IJCCS (Indonesian Journal of Computing and Cybernetics Systems) entity profiling topic modeling sentiment analysis lda svm |
| title | Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| title_full | Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| title_fullStr | Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| title_full_unstemmed | Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| title_short | Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| title_sort | entity profiling to identify actor involvement in topics of social media content |
| topic | entity profiling topic modeling sentiment analysis lda svm |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/59869 |
| work_keys_str_mv | AT pujiwinarcahyo entityprofilingtoidentifyactorinvolvementintopicsofsocialmediacontent AT muhammadhabibi entityprofilingtoidentifyactorinvolvementintopicsofsocialmediacontent |