UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
José Ignacio Abreu, Iván Castro, Claudia Martínez-Araneda, Sebastián Oliva, Yoan Gutiérrez · 2017
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 SentiWord-Net as a polarity lexicon.It achieves an F 1 = 0.624 of average.Among 39 submissions to this task, we ranked 10th.