Twitter analysis for depression on social networks based on sentiment and stress
Paper
Paper/Presentation Title | Twitter analysis for depression on social networks based on sentiment and stress |
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Presentation Type | Paper |
Authors | Tao, Xiaohui (Author), Dharmalingam, Ravi (Author), Zhang, Ji (Author), Zhou, Xujuan (Author), Li, Lin (Author) and Gururajan, Raj (Author) |
Journal or Proceedings Title | Proceedings of the 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) |
Article Number | 8963550 |
Number of Pages | 4 |
Year | 2019 |
Place of Publication | United States |
ISBN | 9781728147628 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BESC48373.2019.8963550 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8963550 |
Conference/Event | 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) |
Event Details | 6th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2019) Parent International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Event Date 28 to end of 30 Oct 2019 Event Location Beijing, China |
Abstract | Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multi-step approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique. |
Keywords | Twitter, depression, sentiment, stress, topic model |
ANZSRC Field of Research 2020 | 461002. Human information behaviour |
461010. Social and community informatics | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | School of Sciences |
School of Management and Enterprise | |
Wuhan University of Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59wz/twitter-analysis-for-depression-on-social-networks-based-on-sentiment-and-stress
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