@inproceedings{htait-2018-adapted,
    title = "Adapted Sentiment Similarity Seed Words For {F}rench Tweets' Polarity Classification",
    author = "Htait, Amal",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2018.jeptalnrecital-deft.12/",
    pages = "323--328",
    abstract = "We present, in this paper, our contribution in DEFT 2018 task 2 : {\textquotedblleft}Global polarity{\textquotedblright}, 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."
}