ItEM is a a high-coverage emotion lexicon for Italian in which each target term is provided with an association score with the basic emotions defined the in Plutchik (1994)’s taxonomy: JOY, SADNESS, ANGER, FEAR, TRUST, DISGUST, SURPRISE, ANTICIPATION.
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the list of seed words collected in Passaro et al., 2015. The seeds are provided both as lemmas and tokens
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the pre-compiled emotive lexicon described in Passaro and Lenci (2016) and referred as SintParModel
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the pre-compiled emotive lexicon built by exploiting count vectors extracted from FB-NEWS15 (Passaro et al., 2016);
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a simplified implementation of ItEM that can be used to create new lexica from a list of seeds and a list of word embeddings.
The code in this repository is compatible with Python3.x and depends on these libraries:
- Numpy
- Scipy
- Scikit-learn
- Gensim
- Pandas
We recommend to use a virtual environment and to install the specific versions of each library provided in the requirements file
Usage is described in the jupyter notebook ItEM
To use ItEM, just clone or download the repository. If you download it as ZIP, make sure to manually download the vector file and place it the vector directory by overwriting the existing file.
@inproceedings{DBLP:conf/lrec/PassaroL16,
author = {Lucia C. Passaro and
Alessandro Lenci},
editor = {Nicoletta Calzolari and
Khalid Choukri and
Thierry Declerck and
Sara Goggi and
Marko Grobelnik and
Bente Maegaard and
Joseph Mariani and
H{\'{e}}l{\`{e}}ne Mazo and
Asunci{\'{o}}n Moreno and
Jan Odijk and
Stelios Piperidis},
title = {Evaluating Context Selection Strategies to Build Emotive Vector Space
Models},
booktitle = {Proceedings of the Tenth International Conference on Language Resources
and Evaluation {LREC} 2016, Portoro{\v{z}}, Slovenia, May 23-28, 2016},
publisher = {European Language Resources Association {(ELRA)}},
year = {2016},
url = {http://www.lrec-conf.org/proceedings/lrec2016/summaries/637.html},
timestamp = {Mon, 19 Aug 2019 15:22:52 +0200},
biburl = {https://dblp.org/rec/conf/lrec/PassaroL16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Passaro et al., (2015): ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon
Passaro, Lenci (2016): Evaluating Context Selection Strategies to Build Emotive Vector Space Models
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