Comparing Supervised Learning Methods for Classifying Spanish Tweets
نویسندگان
چکیده
This paper presents a set of experiments to address the global polarity classification task of Spanish Tweets of TASS 2015. In this work, we compare the main supervised classification algorithms for Sentiment Analysis: Support Vector Machines, Naive Bayes, Maximum Entropy and Decision Trees. We propose to improve the performance of these classifiers using a class reduction technique and then a voting algorithm called Naive Voting. Results show that our proposal outperforms the other machine learning methods proposed in this work.
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