<abstract xmlns="http://www.w3.org/1999/xhtml"><p><bold>Background:</bold> Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. <bold>Objectives:</bold> We focus on the text classification for Albanian news articles using two approaches. <bold>Methods/Approach:</bold> In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. <bold>Results:</bold> Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. <bold>Conclusions:</bold> News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.</p></abstract>

Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.

Albanian Text Classification: Bag of Words Model and Word Analogies

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Albanian Text Classification: Bag of Words Model and Word Analogies"}

Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined...

Albanian Text Classification: Bag of Words Model and Word Analogies

Published Online: May 09, 2019

Page range: 74 - 87

Received: Dec 01, 2017

Accepted: Feb 22, 2018

DOI: https://doi.org/10.2478/bsrj-2019-0006

Keywords
data mining, text classification, news articles, machine learning

© 2019 Arbana Kadriu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Albanian Text Classification: Bag of Words Model and Word Analogies

Published Online: May 09, 2019

Page range: 74 - 87

Received: Dec 01, 2017

Accepted: Feb 22, 2018

DOI: https://doi.org/10.2478/bsrj-2019-0006

Keywordsdata mining, text classification, news articles, machine learning

© 2019 Arbana Kadriu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
data mining, text classification, news articles, machine learning