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Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), Universidad de Santiago de Compostela, Rua de Jenaro de la Fuente Domínguez, 15782 Santiago de Compostela, Spain
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Information 2019, 10(1), 16; https://doi.org/10.3390/info10010016
Received: 16 November 2018 / Revised: 14 December 2018 / Accepted: 24 December 2018 / Published: 4 January 2019
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
In 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 combinations. View Full-Text
Keywords: sentiment analysis; opinion mining; linguistic features; classification; very negative opinions sentiment analysis; opinion mining; linguistic features; classification; very negative opinions
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Almatarneh, S.; Gamallo, P. Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions. Information 2019, 10, 16.

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