Al-Matarneh Mohammad Ata, SattamGamallo Otero, Pablo2020-11-262020-11-262019Almatarneh, S.; Gamallo, P. Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions. Information 2019, 10, 16http://hdl.handle.net/10347/23825In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinationseng© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Sentiment analysisOpinion miningLinguistic featuresClassificationVery negative opinionsjournal article10.3390/info100100162078-2489open access