scholarly journals Evaluating Richer Features and Varied Machine Learning Models for Subjectivity Classification of Book Review Sentences in Portuguese

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 437
Author(s):  
Luana Balador Belisário ◽  
Luiz Gabriel Ferreira ◽  
Thiago Alexandre Salgueiro Pardo
Keyword(s):  
Machine Learning ◽  
Research Field ◽  
Learning Methods ◽  
Feature Sets ◽  
Machine Learning Methods ◽  
Text Passage ◽  
Discourse Relations ◽  
Source Of Information

Texts published on social media have been a valuable source of information for companies and users, as the analysis of this data helps improving/selecting products and services of interest. Due to the huge amount of data, techniques for automatically analyzing user opinions are necessary. The research field that investigates these techniques is called sentiment analysis. This paper focuses specifically on the task of subjectivity classification, which aims to predict whether a text passage conveys an opinion. We report the study and comparison of machine learning methods of different paradigms to perform subjectivity classification of book review sentences in Portuguese, which have shown to be a challenging domain in the area. Specifically, we explore richer features for the task, using several lexical, centrality-based and discourse features. We show the contributions of the different feature sets and evidence that the combination of lexical, centrality-based and discourse features produce better results than any of the feature sets individually. Additionally, by analyzing the achieved results and the acquired knowledge by some symbolic machine learning methods, we show that some discourse relations may clearly signal subjectivity. Our corpus annotation also reveals some distinctive discourse structuring patterns for sentence subjectivity.

Machine Learning methods for micro-FTIR imaging classification of human skin tumors

2021 ◽  
Author(s):  
Matheus del Valle ◽  
Kleber Stancari ◽  
Pedro Arthur Augusto de Castro ◽  
Moises Oliveira dos Santos ◽  
Denise Maria Zezell
Keyword(s):  
Machine Learning ◽  
Human Skin ◽  
Skin Tumors ◽  
Learning Methods ◽  
Ftir Imaging ◽  
Machine Learning Methods ◽  
Imaging Classification

scholarly journals Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

ACS Omega ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 15837-15849 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Machine Learning ◽  
Protease Inhibitors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Hiv 1

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

Classification of beta‐site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods

2021 ◽  
Author(s):  
Ravi Singh ◽  
Ankit Ganeshpurkar ◽  
Powsali Ghosh ◽  
Ankit Vyankatrao Pokle ◽  
Devendra Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amyloid Precursor Protein ◽  
Precursor Protein ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

2020 ◽  
Vol 493 (3) ◽  
pp. 4209-4228 ◽  
Author(s):  
Ting-Yun Cheng ◽  
Christopher J Conselice ◽  
Alfonso Aragón-Salamanca ◽  
Nan Li ◽  
Asa F L Bluck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Data ◽  
Morphological Classification ◽  
Learning Methods ◽  
Visual Classification ◽  
Machine Learning Methods ◽  
Dark Energy Survey ◽  
Galaxy Classification ◽  
Energy Survey

ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ∼2800 galaxies with visual classification from GZ1, we reach an accuracy of ∼0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ∼2.5 per cent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

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