Arabic Text Categorization Using Logistic Regression

Al-Tahrawi, Mayy M.

doi:10.5815/ijisa.2015.06.08

Cited by 19 publications

(14 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have proved that the Logistic Regression English text classifier has a performance which is similar to that of SVMs [46], [47]. LR was used for the first time for Arabic TC by [28] and have proved to be a good performer in this area.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…Recently, the author in [28] was the first researcher to test Logistic Regression (LR) in Arabic TC. She tested the algorithm on Alj-News dataset and proved that Logistic Regression is an efficient Arabic text categorization algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…RBF artificial neural network model performed well in English TC [14], [38], [39], as well as in Arabic TC [21], [22], [28]. The RBF network structure consists of one hidden layer of units which are locally-tuned and fully interconnected to the output layer.…”

Section: Radial Basis Function (Rbf) Networkmentioning

confidence: 99%

“…In each class, 80% of the texts are used for training and 20% for testing. Many researches in Arabic TC have adopted this dataset using different algorithms [49], [20], [8], [13], [28].…”

Section: The Datasetmentioning

confidence: 99%

See 3 more Smart Citations

Polynomial Neural Networks versus Other Arabic Text Classifiers

Al-Tahrawi¹

2016

JSW

View full text Add to dashboard Cite

Many Text Classification (TC) algorithms have been proposed for Arabic TC. Polynomial Neural Networks (PNNs) were used recently in English TC, and have proved to be competitive to the state of the art text classifiers in this field. Lately, they were proposed for classifying Arabic documents. In this research paper, an experimental study that directly compares PNNs against five famous classification algorithms in TC is conducted on Aljazeera-News Arabic dataset. All experiments use the same TC settings, like preprocessing, Feature Selection (FS) and reduction criteria, feature weighting and classifier performance evaluation measures. These algorithms are: SVM (Support Vector Machines), NB (Naive Bayes), kNN (k-Nearest-Neighbor), LR (Logistic Regression) and RBF (Radial Basis Function networks). Results reached in this study reveal that PNN are competitive classifiers in the field of Arabic TC.

show abstract

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Radial Basis Function (Rbf) Networkmentioning

confidence: 99%

“…In each class, 80% of the texts are used for training and 20% for testing. Many researches in Arabic TC have adopted this dataset using different algorithms [49], [20], [8], [13], [28].…”

Section: The Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Polynomial Neural Networks versus Other Arabic Text Classifiers

Al-Tahrawi¹

2016

JSW

View full text Add to dashboard Cite

show abstract

“…The authors carried out voice analysis for telediagnosis of Parkinson disease, using Artificial Neural Networks and Support Vector Machines [17]. Finally, Al-Tahrawi performed the Arabic text categorization by using Logistic Regression method [18].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Akyol¹

2017

IJITCS

View full text Add to dashboard Cite

Abstract-ParkinsonDisease is a neurological disorder, which is one of the most painful, dangerous and noncurable diseases, which occurs at older ages. The Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the Random Forest, Logistic Regression and Artificial Neural Networks classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Also, new data analysis was carried out. According to the results obtained, Artificial Neural Networks is more successful than Random Forest and Logistic Regression algorithms in analysis of new data.

show abstract

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

Rollo

Bonisoli

2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including text categorization. The scope of this paper is to explore the use of word embeddings for Italian crime news text categorization. The approach followed is to compare different document pre-processing, Word2Vec models and methods to obtain word embeddings, including the extraction of bigrams and keyphrases. Then, supervised and unsupervised Machine Learning categorization algorithms have been applied and compared. In addition, the imbalance issue of the input dataset has been addressed by using Synthetic Minority Oversampling Technique (SMOTE) to oversample the elements in the minority classes. Experiments conducted on an Italian dataset of 17,500 crime news articles collected from 2011 till 2021 show very promising results. The supervised categorization has proven to be better than the unsupervised categorization, overcoming 80% both in precision and recall, reaching an accuracy of 0.86. Furthermore, lemmatization, bigrams and keyphrase extraction are not so decisive. In the end, the availability of our model on GitHub together with the code we used to extract word embeddings allows replicating our approach to other corpus either in Italian or other languages.

show abstract

Arabic Text Categorization Using Logistic Regression

Cited by 19 publications

References 42 publications

Polynomial Neural Networks versus Other Arabic Text Classifiers

Polynomial Neural Networks versus Other Arabic Text Classifiers

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

Contact Info

Product

Resources

About