Trained recurrent neural network (RNN) models for predicting emotions from English tweets. Our models work on characters hence we pass the whole tweet without any preprocessing as an input to the RNN. We are predicting three emotion classifications:
- Ekman's six basic emotions,
- Plutchik's eight basic emotions,
- Profile of Mood States (POMS) six mood states.
The following examples show how to predict Ekman's emotions from tweet's content.
First let's import EmotionPredictor.
>>> from emotion_predictor import EmotionPredictorNext we instantiate the model and define our tweets.
In this example we will work with Ekman's emotions.
Use plutchik to predict Plutchik's emotions or poms for Profile of Mood States.
To use models in multilabel setting instead of multiclass provide ml as the setting argument.
>>> model = EmotionPredictor(classification='ekman', setting='mc')
>>> tweets = [
"Watching the sopranos again from start to finish!",
"Finding out i have to go to the dentist tomorrow",
"I want to go outside and chalk but I have no chalk",
"I HATE PAPERS AH #AH #HATE",
"My mom wasn't mad",
"Do people have no Respect for themselves or you know others peoples homes",
]We obtain model's predictions by calling predict_classes method:
>>> model.predict_classes(tweets)
Tweet Emotion
0 Watching the sopranos again from start to finish! Joy
1 Finding out i have to go to the dentist tomorrow Fear
2 I want to go outside and chalk but I have no chalk Sadness
3 I HATE PAPERS AH #AH #HATE Anger
4 My mom wasn't mad Surprise
5 Do people have no Respect for themselves or you know others peoples homes DisgustTo observe probabilities for each class use predict_probabilities method:
>>> model.predict_probabilities(tweets)
Tweet Anger Disgust Fear Joy Sadness Surprise
0 Watching the sopranos again from start to finish! 0.000717 0.000244 0.003829 0.946539 0.005610 0.043061
1 Finding out i have to go to the dentist tomorrow 0.007705 0.000039 0.783890 0.198629 0.008950 0.000787
2 I want to go outside and chalk but I have no c... 0.002772 0.000095 0.004137 0.025035 0.963712 0.004249
3 I HATE PAPERS AH #AH #HATE 0.956343 0.006368 0.031387 0.000350 0.004375 0.001176
4 My mom wasn't mad 0.063969 0.004990 0.013971 0.079884 0.218708 0.618478
5 Do people have no Respect for themselves or yo... 0.070003 0.801428 0.067724 0.003646 0.038480 0.018718If you would rather just use the final hidden state representation call embedd:
>>> model.embedd(tweets)
Tweet Dim1 Dim2 ... Dim798 Dim799 Dim800
0 Watching the sopranos again from start to finish! -0.128762 -0.000000 ... -0.260896 -0.009062 -0.110209
1 Finding out i have to go to the dentist tomorrow -0.525602 0.407847 ... -0.000088 -0.001489 0.142871
2 I want to go outside and chalk but I have no c... -0.057850 0.566420 ... -0.091341 -0.003914 -0.037481
3 I HATE PAPERS AH #AH #HATE 0.019670 -0.288512 ... 0.100234 0.013350 -0.014305
4 My mom wasn't mad -0.004135 0.657584 ... -0.029319 -0.007455 -0.066208
5 Do people have no Respect for themselves or yo... -0.246179 0.069080 ... 0.029919 0.011467 -0.000520
[6 rows x 801 columns]- demo.py: script is showing how to use our models for predicting emotions or embedding tweets.
- emotion_prediction.py: helper scripts that defines EmotionPredictor class.
- models/: contains trained RNN models.
If you use our models in a scientific publication, we would appreciate citations to the following paper:
Colnerič, N., & Demšar, J. (2018). Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. IEEE Transactions on Affective Computing, PP (99), 1. https://doi.org/10.1109/TAFFC.2018.2807817