Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Li, Jing Amelia (COMP) | en_US |
| dc.creator | Ding, Keyang | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11369 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Hashtags, emotions, and comments : a large-scale dataset to understand fine-grained social emotions to online topics | en_US |
| dcterms.abstract | This paper studies social emotions to online discussion topics. While most prior work focus on emotions from writers, we investigate readers' responses and explore the public feelings to an online topic. A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. In experiments, we examine baseline performance to predict a topic's possible social emotions in a multi-label classification setting. The results show that a seq2seq model with user comment modeling performs the best, even surpassing human prediction. More analyses shed light on the effects of emotion types, topic description lengths, contexts from user comments and the limited capacity of the existing models. | en_US |
| dcterms.extent | x, 46 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Online social networks -- Psychological aspects | en_US |
| dcterms.LCSH | Emotions | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5817.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.81 MB | Adobe PDF | View/Open |
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