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I'm implementing a system that could detect the human emotion in text. Are there any manually annotated data sets available for supervised learning and testing?
Here are some interesting datasets: https://dataturks.com/projects/trending
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Emotion can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Emotion Detection in text documents is essentially a content - based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning.
Three popular ML algorithms, SVM, RF, and kNN were used for emotion intensity recognition. A comparative study and implementation of algorithms for measuring facial emotions and their intensities based on the different AUs (Action Units) are presented.
CSAIL's new device, dubbed the EQ-Radio, extracts its data from wireless signals, making it more convenient and efficient than existing methods within the global emotion-detection space, which rely on on-body sensors or facial-recognition technology.
The Complex Emotion Expression Database (CEED) includes 480 images of eight young adult actors creating six basic and nine complex social sexual emotional expressions. The actors are both female and male with some racial diversity.
Below here, we are listing down top eight datasets, in no particular order, that are available for emotion detection. AffectNet is one of the largest datasets for facial affect in still images which covers both categorical and dimensional models.
The Datasets for Sentiment Analysis The development of a system for the detection of emotions starts with the identification of a labeled dataset. This dataset must respect the specific set of assumptions we decided to adopt for our system. Let’s see here the most common ones, and identify the tasks for which we can use them:
Emotion Detection and Recognition from text is a recent field of research that is closely related to Sentiment Analysis. Sentiment Analysis aims to detect positive, neutral, or negative feelings from text, whereas Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear ...
Emotion detection from text is one of the challenging problems in Natural Language Processing. The reason is the unavailability of labeled dataset and the multi-class nature of the problem. Humans have a variety of emotions and it is difficult to collect enough records for each emotion and hence the problem of class imbalance arises.
The field of textual emotion detection is still very new and the literature is fragmented in many different journals of different fields. Its really hard to get a good look on whats out there.
Note that there a several emotion theories psychology. Hence there a different ways of modeling/representing emotions in computing. Most of the times "emotion" refers to a phenomena such as anger, fear or joy. Other theories state that all emotions can be represented in a multi-dimensional space (so there is an infinite number of them).
Here are a some (publicly available) data sets I know of (updated):
EmoBank. 10k sentences annotated with Valence, Arousal and Dominance values (disclosure: I am one of the authors). https://github.com/JULIELab/EmoBank
The "Emotion Intensity in Tweets" data set from the WASSA 2017 shared task. http://saifmohammad.com/WebPages/EmotionIntensity-SharedTask.html
The Valence and Arousal Facebook Posts by Preotiuc-Pietro and others: http://wwbp.org/downloads/public_data/dataset-fb-valence-arousal-anon.csv
The Affect data by Cecilia Ovesdotter Alm: http://people.rc.rit.edu/~coagla/affectdata/index.html
The Emotion in Text data set by CrowdFlower https://www.crowdflower.com/wp-content/uploads/2016/07/text_emotion.csv
ISEAR: http://emotion-research.net/toolbox/toolboxdatabase.2006-10-13.2581092615
Test Corpus of SemEval 2007 (Task on Affective Text) http://web.eecs.umich.edu/~mihalcea/downloads.html
A reannotation of the SemEval Stance data with emotions: http://www.ims.uni-stuttgart.de/data/ssec
If you want to go deeper into the topic, here are some surveys I recommend (disclosure: I authored the first one).
Buechel, S., & Hahn, U. (2016). Emotion Analysis as a Regression Problem — Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation. In ECAI 2016.22nd European Conference on Artificial Intelligence (pp. 1114–1122). The Hague, Netherlands (available: http://ebooks.iospress.nl/volumearticle/44864).
Canales, L., & Martínez-Barco, P. (n.d.). Emotion Detection from text: A Survey. Processing in the 5th Information Systems Research Working Days (JISIC 2014), 37 (available: http://www.aclweb.org/anthology/W14-6905).
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