Abstract
We present our supervised sentiment classification system which competed in SemEval-2015 Task 10B: Sentiment Classification in Twitter- Message Polarity Classification. Our system employs a Support Vector Machine classifier trained using a number of features including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. Using weighted Support Vector Machines, to address the issue of class imbalance, our system obtains positive class F-scores of 0.701 and 0.656, and negative class F-scores of 0.515 and 0.478 over the training and test sets, respectively.
Cite
CITATION STYLE
Sarker, A., Nikfarjam, A., Weissenbacher, D., & Gonzalez, G. (2015). DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 510–514). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2085
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