Computer Science > Human-Computer Interaction
arXiv:2303.06632 (cs)
[Submitted on 12 Mar 2023]
Title:Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention
View a PDF of the paper titled Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention, by Soujanya Narayana and 3 other authors
View PDFAbstract:While emotion and mood interchangeably used, they differ in terms of duration, intensity and attributes. Even as multiple psychology studies examine the mood-emotion relationship, mood prediction has barely been studied. Recent machine learning advances such as the attention mechanism to focus on salient parts of the input data, have only been applied to infer emotions rather than mood. We perform mood prediction by incorporating both mood and emotion change information. We additionally explore spatial and temporal attention, and parallel/sequential arrangements of the spatial and temporal attention modules to improve mood prediction performance. To examine generalizability of the proposed method, we evaluate models trained on the AFEW dataset with EMMA. Experiments reveal that (a) emotion change information is inherently beneficial to mood prediction, and (b) prediction performance improves with the integration of sequential and parallel spatial-temporal attention modules.
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2303.06632 [cs.HC] |
| (orarXiv:2303.06632v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2303.06632 arXiv-issued DOI via DataCite |
Submission history
From: Soujanya Narayana [view email][v1] Sun, 12 Mar 2023 11:04:18 UTC (1,941 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention, by Soujanya Narayana and 3 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.