Emerging Research Topic Detection Using Filtered-LDA
Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to...
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2021
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2987 https://doi.org/10.3390/ai2040035. |
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| _version_ | 1862980619815878656 |
|---|---|
| author | Alattar, Fuad |
| author2 | Shaalan, Khaled |
| author2_role | author |
| author_facet | Alattar, Fuad Shaalan, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Alattar, Fuad Shaalan, Khaled |
| dc.date.none.fl_str_mv | 2021 2025-05-13T13:20:05Z 2025-05-13T13:20:05Z |
| dc.identifier.none.fl_str_mv | Alattar, F. and Shaalan, K. (2021) “Emerging Research Topic Detection Using Filtered-LDA,” AI, 2(4), pp. 578–599. 2673-2688 https://bspace.buid.ac.ae/handle/1234/2987 https://doi.org/10.3390/ai2040035. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | AIv2 n4 (20211031): 578-599 |
| dc.subject.none.fl_str_mv | emerging topic detection; research trend detection; topic discovery; topic modeling; hot topics; trending topics; FB-LDA; Filtered-LDA |
| dc.title.none.fl_str_mv | Emerging Research Topic Detection Using Filtered-LDA |
| dc.type.none.fl_str_mv | Article |
| description | Comparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic. |
| id | budr_e57d8e321663dc1cc2d7ee888d8d6fbe |
| identifier_str_mv | Alattar, F. and Shaalan, K. (2021) “Emerging Research Topic Detection Using Filtered-LDA,” AI, 2(4), pp. 578–599. 2673-2688 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2987 |
| publishDate | 2021 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Emerging Research Topic Detection Using Filtered-LDAAlattar, FuadShaalan, Khaledemerging topic detection; research trend detection; topic discovery; topic modeling; hot topics; trending topics; FB-LDA; Filtered-LDAComparing two sets of documents to identify new topics is useful in many applications, like discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches to address this task are examined to identify limitations and necessary enhancements. To overcome these limitations, we introduce two separate frameworks to discover emerging topics through a filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old topics from a timestamped set of documents, removes all documents that focus on old topics, and keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using keywords from old topics to represent emerging topics. The final stage of the filter uses multiple topic visualization formats to improve human interpretability of the filtered topics, and it presents the most-representative document for each topic.MDPI2025-05-13T13:20:05Z2025-05-13T13:20:05Z2021ArticleAlattar, F. and Shaalan, K. (2021) “Emerging Research Topic Detection Using Filtered-LDA,” AI, 2(4), pp. 578–599.2673-2688https://bspace.buid.ac.ae/handle/1234/2987https://doi.org/10.3390/ai2040035.enAIv2 n4 (20211031): 578-599oai:bspace.buid.ac.ae:1234/29872025-05-13T13:30:50Z |
| spellingShingle | Emerging Research Topic Detection Using Filtered-LDA Alattar, Fuad emerging topic detection; research trend detection; topic discovery; topic modeling; hot topics; trending topics; FB-LDA; Filtered-LDA |
| title | Emerging Research Topic Detection Using Filtered-LDA |
| title_full | Emerging Research Topic Detection Using Filtered-LDA |
| title_fullStr | Emerging Research Topic Detection Using Filtered-LDA |
| title_full_unstemmed | Emerging Research Topic Detection Using Filtered-LDA |
| title_short | Emerging Research Topic Detection Using Filtered-LDA |
| title_sort | Emerging Research Topic Detection Using Filtered-LDA |
| topic | emerging topic detection; research trend detection; topic discovery; topic modeling; hot topics; trending topics; FB-LDA; Filtered-LDA |
| url | https://bspace.buid.ac.ae/handle/1234/2987 https://doi.org/10.3390/ai2040035. |