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ID 113915
Author
Bando, Koji NTT Plala
Keywords
Hobby estimation
deep neural networks
sequential statements
social media
Content Type
Journal Article
Description
With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users’ interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the user’s hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm.
Journal Title
International Journal of Machine Learning and Computing
ISSN
20103700
Volume
9
Issue
2
Start Page
108
End Page
114
Published Date
2019-04
Rights
This article is licensed under an open access Creative Commons CC BY 4.0 license(https://creativecommons.org/licenses/by/4.0/), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited.
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DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology