Scripts for WASSA-2017 Shared Task on Emotion Intensity
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submission-dev
submission-svm
.gitignore
LICENSE
README.md
codalab_dev_baseline.sh
dataset_stats.ipynb
demo.sh
evaluate.py
feature_selection.ipynb
fix_weka_output.py
liwc.py
liwc_svm-gold.ipynb
liwc_svm-test.ipynb
liwc_svm.ipynb
santos2017pln.bib
tfidf_svm.ipynb
try_filter.sh
tweets_to_arff.py

README.md

PLN-PUCRS at EmoInt-2017

Psycholinguistic features for emotion intensity prediction in tweets

Linguistic Inquiry and Word Count (LIWC) is a rich dictionary that map words into several psychological categories such as Affective, Social, Cognitive, Perceptual and Biological processes. In this work, we have used LIWC psycholinguistic categories to train regression models and predict emotion intensity in tweets for the EmoInt-2017 task. Results show that LIWC features may boost emotion intensity prediction on the basis of a low dimension set.

Full text , Bibtex

EmoInt using LIWC

Scripts for for the WASSA-2017 Shared Task on Emotion Intensity (EmoInt).

1. Evaluation Script

The evaluation script liwc_svm-test.ipynb calculates the following four measures between the gold standard scores and the given predictions.

PUCRS NLP Group

This project belongs to NLP Group at PUCRS, Brazil