The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Emotions from text: machine learning for text-based emotion prediction
Open accessAbstract
In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
The language of emotions: An analysis of a semantic field
Johnson-laird, P. N., & Oatley, K. (1989) · Cognition & Emotion · doi:10.1080/02699938908408075 · Cited by 736
Cited by
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
GoEmotions: A Dataset of Fine-Grained Emotions
Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., & Ravi, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.372 · Cited by 873
Emotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755
Metadata
- Authors
- Alm, C. O., Roth, D., & Sproat, R.
- Venue
- Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05
- Published
- 2005
- Pages
- 579-586
- OpenAlex
- W2168493061
- Cited by
- 781
- References
- 2
- FWCI
- 5.01
- Type
- conference paper
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emotions from text: machine learning for text-based emotion prediction. https://app.cochat.ai/papers/10.3115/1220575.1220648
- The paper
- Alm, C. O., Roth, D., & Sproat, R. (2005). Emotions from text: machine learning for text-based emotion prediction. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. https://doi.org/10.3115/1220575.1220648
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
