Abstract
We present, in this paper, our contribution in DEFT 2018 task 2 : “Global polarity”, determining the overall polarity (Positive, Negative, Neutral or MixPosNeg) of tweets regarding public transport, in French language. Our system is based on a list of sentiment seed-words adapted for French public transport tweets. These seed-words are extracted from DEFT’s training annotated dataset, and the sentiment relations between seed-words and other terms are captured by cosine measure of their word embeddings representations, using a French language word embeddings model of 683k words. Our semi-supervised system achieved an F1-measure equals to 0.64.- Anthology ID:
- 2018.jeptalnrecital-deft.12
- Volume:
- Actes de la Conférence TALN. Volume 2 - Démonstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT
- Month:
- 5
- Year:
- 2018
- Address:
- Rennes, France
- Editors:
- Pascale Sébillot, Vincent Claveau
- Venue:
- JEP/TALN/RECITAL
- SIG:
- Publisher:
- ATALA
- Note:
- Pages:
- 323–328
- Language:
- URL:
- https://aclanthology.org/2018.jeptalnrecital-deft.12
- DOI:
- Cite (ACL):
- Amal Htait. 2018. Adapted Sentiment Similarity Seed Words For French Tweets’ Polarity Classification. In Actes de la Conférence TALN. Volume 2 - Démonstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT, pages 323–328, Rennes, France. ATALA.
- Cite (Informal):
- Adapted Sentiment Similarity Seed Words For French Tweets’ Polarity Classification (Htait, JEP/TALN/RECITAL 2018)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2018.jeptalnrecital-deft.12.pdf