TY - GEN
T1 - Mining Web Log Data for News Topic Modeling Using Latent Dirichlet Allocation
AU - Surjandari, Isti
AU - Rosyidah, Asma
AU - Zulkarnain, Zulkarnain
AU - Laoh, Enrico
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The growth of e-news platforms, the most popular and accessible media for sharing information, has resulted in the increase of digital news articles volume. Users' navigation across news articles in e-news platform, which is captured in form of web log data, is able to show which articles are read by users. News articles read by users can illustrate topics of interest and public unrest towards a particular event, field, or aspect. The knowledge and understanding of topics of interest and public unrest are important, especially for subsequent newsletter journalists and government in policy-making. This study was conducted in response to the importance of extracting topics from news articles read by users or public. Latent dirichlet allocation was used as topic modeling algorithm from list of news article title and category obtained from user web log data across 5 e-news publisher domains in Indonesia. The topic modeling process results in 12 topics of news articles. The results of this study provide insight to e-news platform regarding the reading material focus of users.
AB - The growth of e-news platforms, the most popular and accessible media for sharing information, has resulted in the increase of digital news articles volume. Users' navigation across news articles in e-news platform, which is captured in form of web log data, is able to show which articles are read by users. News articles read by users can illustrate topics of interest and public unrest towards a particular event, field, or aspect. The knowledge and understanding of topics of interest and public unrest are important, especially for subsequent newsletter journalists and government in policy-making. This study was conducted in response to the importance of extracting topics from news articles read by users or public. Latent dirichlet allocation was used as topic modeling algorithm from list of news article title and category obtained from user web log data across 5 e-news publisher domains in Indonesia. The topic modeling process results in 12 topics of news articles. The results of this study provide insight to e-news platform regarding the reading material focus of users.
KW - e-news
KW - latent dirichlet allocation
KW - topic modeling
KW - web log data
UR - http://www.scopus.com/inward/record.url?scp=85062094372&partnerID=8YFLogxK
U2 - 10.1109/ICISCE.2018.00076
DO - 10.1109/ICISCE.2018.00076
M3 - Conference contribution
AN - SCOPUS:85062094372
T3 - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
SP - 331
EP - 335
BT - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
A2 - Li, Shaozi
A2 - Dai, Ying
A2 - Cheng, Yun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Science and Control Engineering, ICISCE 2018
Y2 - 20 July 2018 through 22 July 2018
ER -