@inproceedings{balikas-2017-twise,
    title = "{T}wi{S}e at {S}em{E}val-2017 Task 4: Five-point {T}witter Sentiment Classification and Quantification",
    author = "Balikas, Georgios",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/S17-2127/",
    doi = "10.18653/v1/S17-2127",
    pages = "755--759",
    abstract = "The paper describes the participation of the team {\textquotedblleft}TwiSE{\textquotedblright} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {\textquotedblleft}Sentiment Analysis in Twitter{\textquotedblright} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E."
}