@inproceedings{abreu-etal-2017-ucsc,
title = "{UCSC}-{NLP} at {S}em{E}val-2017 Task 4: Sense n-grams for Sentiment Analysis in {T}witter",
author = "Abreu, Jos{\'e} and
Castro, Iv{\'a}n and
Mart{\'\i}nez, Claudia and
Oliva, Sebasti{\'a}n and
Guti{\'e}rrez, Yoan",
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://aclanthology.org/S17-2136",
doi = "10.18653/v1/S17-2136",
pages = "807--811",
abstract = "This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an $F_1=0.624$of average. Among 39 submissions to this task, we ranked 10th.",
}
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%0 Conference Proceedings
%T UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
%A Abreu, José
%A Castro, Iván
%A Martínez, Claudia
%A Oliva, Sebastián
%A Gutiérrez, Yoan
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F abreu-etal-2017-ucsc
%X This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an F₁=0.624of average. Among 39 submissions to this task, we ranked 10th.
%R 10.18653/v1/S17-2136
%U https://aclanthology.org/S17-2136
%U https://doi.org/10.18653/v1/S17-2136
%P 807-811
Markdown (Informal)
[UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter](https://aclanthology.org/S17-2136) (Abreu et al., SemEval 2017)
ACL