Abstract
We present an approach for tackling the Sentiment Analysis problem in SemEval 2015. The approach is based on the use of a co-occurrence graph to represent existing relationships among terms in a document with the aim of using centrality measures to extract the most representative words that express the sentiment. These words are then used in a supervised learning algorithm as features to obtain the polarity of unknown documents. The best results obtained for the different datasets are: 77.76% for positive, 100% for negative and 68.04% for neutral, showing that the proposed graph-based representation could be a way of extracting terms that are relevant to detect a sentiment.
Cite
CITATION STYLE
Castillo, E., Cervantes, O., Vilariño, D., Báez, D., & Sánchez, A. (2015). UDLAP: Sentiment Analysis Using a Graph Based Representation. 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. 556–560). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2093
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