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Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
Version 1
: Received: 16 November 2018 / Approved: 19 November 2018 / Online: 19 November 2018 (09:35:13 CET)
A peer-reviewed article of this Preprint also exists.
Almatarneh, S.; Gamallo, P. Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions. Information 2019, 10, 16. Almatarneh, S.; Gamallo, P. Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions. Information 2019, 10, 16.
Journal reference: Information 2019, 10, 16
DOI: 10.3390/info10010016
Abstract
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: Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT).
Keywords
sentiment analysis; opinion mining; linguistic features; classification; very negative opinions
Subject
MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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