Dataset Classification Combining Wide-coverage Lexical Resources and Text Features

In this paper we describe our participation in TASS 2017 shared task on polarity classification of Spanish tweets. For this task we built a classification model based on the Lingmotif Spanish lexicon, and combined this with a number of formal text features, both general and CMC-specific, as well as single-word keywords and n-gram keywords, achieving above-average results across all three datasets. We report the results of our experiments with different combinations of said feature sets and machine learning algorithms (logistic regression and SVM).

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