UDLAP: Sentiment Analysis Using a Graph-Based Representation

Esteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez, Alfredo Sánchez
2015 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)  
We present an approach for tackling the Sentiment Analysis problem in SemEval 2015. The approach is based on the use of a cooccurrence 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%
more » ... sets 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.
doi:10.18653/v1/s15-2093 dblp:conf/semeval/CastilloCVBS15 fatcat:u4ifmx2j3zaa5c5lrtvjaq63i4