UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter

SEMEVAL 2017 · Jos{\'e} Abreu, Iv{\'a}n Castro, Claudia Mart{\'\i}nez, Sebasti{\'a}n Oliva, Yoan Guti{\'e}rrez ·

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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