@inproceedings{kravchenko-pivovarova-2018-dl,
    title = "{DL} Team at {S}em{E}val-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings",
    author = "Kravchenko, Dmitry  and
      Pivovarova, Lidia",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/S18-1025/",
    doi = "10.18653/v1/S18-1025",
    pages = "172--176",
    abstract = "The paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone."
}