Polarity Classification of Traffic Related Tweets

Mapping Intimacies ◽

10.5753/eniac.2018.4417 ◽

2018 ◽

Author(s):

Clarissa Castellã Xavier

Keyword(s):

Support Vector Machine ◽

Maximum Entropy ◽

Traffic Management ◽

Support Vector ◽

Average Precision ◽

Learning Methods ◽

Management Agency ◽

Average Recall ◽

Polarity Classification

In this paper we present a study about polarity classification of tweets in the traffic domain. Specifically, we use the data in Portuguese language from an account maintained by a traffic management agency. We evaluate the performance of three learning methods: SVM (Support Vector Machine), Naive Bayes and Maximum Entropy. We also explore how the use of balanced vs. unbalanced corpus affects the models behavior. The results show that, in this context, a ML classifier obtains better results than the reported in the literature. In our experiments, SVM trained with a balanced corpus outperforms all tested models, achieving 99% of Accuracy, Average Recall and Average Precision.

Download Full-text

Sentiment polarity classification of tweets using a extended dictionary

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol21iss62pp1-12 ◽

2018 ◽

Vol 21 (62) ◽

pp. 1

Author(s):

Jorge E. Camargo ◽

Vladimir Vargas-Calderon ◽

Nelson Vargas ◽

Liliana Calderón-Benavides

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Classification Performance ◽

Semantic Relations ◽

Support Vector ◽

The Real ◽

Polarity Classification ◽

Real Academia ◽

Word Definitions

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

Download Full-text

Comparative Analysis of Machine Learning Algorithms for Multi-Syndrome Classification of Neurodegenerative Syndromes

10.21203/rs.3.rs-178965/v1 ◽

2021 ◽

Author(s):

Leonie Lampe ◽

Sebastian Niehaus ◽

Hans-Jürgen Huppertz ◽

Alberto Merola ◽

Janis Reinelt ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Performance Measures ◽

Careful Consideration ◽

Machine Learning Algorithms ◽

Classification Task ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods

Abstract Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for predictions of structured neuroimaging data, yet nobody compared them in this context.Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.Design, Setting, and Participants Atlas-based volumetry was performed on multi-centric T1weighted MRI data from 940 subjects, i.e. 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.Interventions n.a.Main Outcomes and Measures Cohen’s Kappa, Accuracy, and F1-score to assess model performance.Results Over all, the neural network produced both the best performance measures as well as the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with wide-spread and rather weak atrophy.Conclusions and Relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.Trial Registration n.a.

Download Full-text

Machine learning for the extragalactic astronomy educational manual

Proceedings of the International Astronomical Union ◽

10.1017/s1743921321000132 ◽

2019 ◽

Vol 15 (S367) ◽

pp. 461-463

Author(s):

Maksym Vasylenko ◽

Daria Dobrycheva

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Programming Language ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Python Programming Language ◽

Python Programming

AbstractWe evaluated a new approach to the automated morphological classification of large galaxy samples based on the supervised machine learning techniques (Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and k-Nearest Neighbours) and Deep Learning using the Python programming language. A representative sample of ∼315000 SDSS DR9 galaxies at z < 0.1 and stellar magnitudes r < 17.7m was considered as a target sample of galaxies with indeterminate morphological types. Classical machine learning methods were used to binary morphologically classification of galaxies into early and late types (96.4% with Support Vector Machine). Deep machine learning methods were used to classify images of galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) with the Xception architecture (94% accuracy for four classes and 88% for cigar-like galaxies). These results created a basis for educational manual on the processing of large data sets in the Python programming language, which is intended for students of the Ukrainian universities.

Download Full-text