RT Journal Article
T1 Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
A1 Al-Matarneh Mohammad Ata, Sattam
A1 Gamallo Otero, Pablo
K1 Sentiment analysis
K1 Opinion mining
K1 Linguistic features
K1 Classification
K1 Very negative opinions
AB 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
PB MDPI
YR 2019
FD 2019
LK http://hdl.handle.net/10347/23825
UL http://hdl.handle.net/10347/23825
LA eng
NO Almatarneh, S.; Gamallo, P. Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions. Information 2019, 10, 16
NO This research was funded by project TelePares (MINECO, ref:FFI2014-51978-C2-1-R), and the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08) and the European Regional Development Fund (ERDF)
DS Minerva
RD 24 abr 2026