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| Paper Abstract and Keywords | ||
| Presentation | 2023-07-13 13:00 Improvements in Depression Detection by Applying a Topic Model on Japanese Tweets Rio Ishibashi, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.)  SeMI2023-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Depression is a prevalent mental health disorder that can have a significant impact on an individual's well-being. Therefore, early detection and treatment is crucial for symptom management. Recently, social media platforms have emerged as a valuable source of data for mental health research, and machine learning techniques can be employed to analyze text to identify potential signs of depression. In this study, we present a model for detecting depression based on Twitter data, which has high usage rates in Japan. We utilized the morphological analyzer Juman++ and an LDA (Latent Dirichlet Allocation) topic model to summarize Japanese tweets. User activity information was also integrated into the feature set. By testing morphological analyzers and the number of words in the dictionary, our model made improvements upon previous models [1]. Our analysis of the tweet and user features revealed that individuals with depression tend to tweet about lifestyle, work, and negative mental states. Furthermore, depressed individuals tended to post more tweets during nighttime, possibly indicating the presence of insomnia. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | depression detection / Japanese / social media / topic modeling / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 110, SeMI2023-29, pp. 34-39, July 2023. | |
| Paper # | SeMI2023-29 | |
| Date of Issue | 2023-07-05 (SeMI) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SeMI2023-29 |
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| Conference Information | |
| Committee | SeMI RCS RCC NS SR |
| Conference Date | 2023-07-12 - 2023-07-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka University Nakanoshima Center + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Distributed Wireless Network, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2023-07-SeMI-RCS-RCC-NS-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improvements in Depression Detection by Applying a Topic Model on Japanese Tweets |
| Sub Title (in English) | |
| Keyword(1) | depression detection |
| Keyword(2) | Japanese |
| Keyword(3) | social media |
| Keyword(4) | topic modeling |
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| 1st Author's Name | Rio Ishibashi |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-07-13 13:00:00 |
| Presentation Time | 25 minutes |
| Registration for | SeMI |
| Paper # | SeMI2023-29 |
| Volume (vol) | vol.123 |
| Number (no) | no.110 |
| Page | pp.34-39 |
| #Pages | 6 |
| Date of Issue | 2023-07-05 (SeMI) |
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