TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION
Dublin Core
Title
TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION
Subject
Deep Learning, Word Embedding, Information Extraction, Event detection, Social Media
Description
Twitter social media is used by millions of users to share stories about their lives. There are millions of
tweets sent by Twitter users in a short amount of time. These tweets can contain information about an incident, complaints from Twitter users, and others. Finding information about events from existing tweets requires great effort. Therefore, this study proposed a system that can detect events based on tweets using the CNN-LSTM architecture. Based on the classification testing obtained precision results of 70.97%, and recall amounted to 63.76%. The results obtained are good enough as a first step to detect events on Twitter.
tweets sent by Twitter users in a short amount of time. These tweets can contain information about an incident, complaints from Twitter users, and others. Finding information about events from existing tweets requires great effort. Therefore, this study proposed a system that can detect events based on tweets using the CNN-LSTM architecture. Based on the classification testing obtained precision results of 70.97%, and recall amounted to 63.76%. The results obtained are good enough as a first step to detect events on Twitter.
Creator
Adenuar Purnomo, Ahmad Afiif Naufal, Ery Permana Yudha, Agus Zainal Arifin
Source
http://dx:doi:org/10:21609/jiki:v13i2.815
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2020-06-30
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Adenuar Purnomo, Ahmad Afiif Naufal, Ery Permana Yudha, Agus Zainal Arifin, “TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION,” Repository Horizon University Indonesia, accessed October 30, 2025, https://repository.horizon.ac.id/items/show/8805.