EmoInt can be used for affective computing like sentiment analysis, emotion classification, emotion intensity computing etc. This project is developed during WASSA 2017 Emotion Intensity Task. It is inspired by AffectiveTweets repo (baseline model for the Emotion Intensity Task). This project contains high level wrappers for combining various word embeddings and scripts for creating ensembles.
Some word-embeddings need to be downloaded separately for using all available featurizers.
Note: Instructions to access these resources can be found here
The relevant word embeddings are:
- NRC Affect Intensity: Link. Download to
emoint/resources/nrc_affect_intensity.txt.gz - NRC Emotion Wordlevel Lexicons: Link. Download to
emoint/resources/NRC-emotion-lexicon-wordlevel-v0.92.txt.gz - Sentiment140: Link. Download to
emoint/resources/Sentiment140-Lexicon-v0.1
The NRC Emotion Wordlevel Lexicons are not in the standard format, we've provided a script to reformat it in the required format.
python emoint/utils/reformat.py emoint/resources/NRC-emotion-lexicon-wordlevel-v0.92.txt.gzThe package can be installed as follows:
python setup.py install
You can learn how to use the featurizers by following these notebooks in examples directory
python -m unittest discover -v
@inproceedings{duppada2017seernet,
title={Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator},
author={Duppada, Venkatesh and Hiray, Sushant},
booktitle={Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
pages={205--211},
year={2017}
}
This is open source work of DeepAffects. DeepAffects is an emotional intelligence analysis engine that measures the effect emotional intelligence has on team dynamics, and provides emotional analytics that serve as the basis of insights to improve project management, performance and satisfaction across organizations, projects, and teams. To watch DeepAffects in action: check out DeepAffects Atlassian JIRA addon and our Github addon.