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...
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| Other Authors: | , |
| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2020-10-31.
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| Online Access: | Get Fulltext Get Fulltext |
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| LEADER | 02601 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_59869 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Cahyo, Puji Winar |e author |
| 100 | 1 | 0 | |a Kemenristekdikti Republic of Indonesia |e contributor |
| 100 | 1 | 0 | |a Center of Study and Data Analytic Services of Universitas Jenderal Achmad Yani Yogyakarta |e contributor |
| 700 | 1 | 0 | |a Habibi, Muhammad |e author |
| 245 | 0 | 0 | |a Entity Profiling to Identify Actor Involvement in Topics of Social Media Content |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2020-10-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/59869 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Entity Profiling; Topic Modeling; Sentiment Analysis; LDA; SVM | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 4 (2020): October; 417-428 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/59869/29704 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/59869 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/59869/29704 |z Get Fulltext |