@inproceedings{mammadli-etal-2019-sentiment,
title = "Sentiment Polarity Detection in {A}zerbaijani Social News Articles",
author = "Mammadli, Sevda and
Huseynov, Shamsaddin and
Alkaramov, Huseyn and
Jafarli, Ulviyya and
Suleymanov, Umid and
Rustamov, Samir",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1082",
doi = "10.26615/978-954-452-056-4_082",
pages = "703--710",
abstract = "Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.",
}
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<abstract>Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.</abstract>
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%0 Conference Proceedings
%T Sentiment Polarity Detection in Azerbaijani Social News Articles
%A Mammadli, Sevda
%A Huseynov, Shamsaddin
%A Alkaramov, Huseyn
%A Jafarli, Ulviyya
%A Suleymanov, Umid
%A Rustamov, Samir
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F mammadli-etal-2019-sentiment
%X Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.
%R 10.26615/978-954-452-056-4_082
%U https://aclanthology.org/R19-1082
%U https://doi.org/10.26615/978-954-452-056-4_082
%P 703-710
Markdown (Informal)
[Sentiment Polarity Detection in Azerbaijani Social News Articles](https://aclanthology.org/R19-1082) (Mammadli et al., RANLP 2019)
ACL
- Sevda Mammadli, Shamsaddin Huseynov, Huseyn Alkaramov, Ulviyya Jafarli, Umid Suleymanov, and Samir Rustamov. 2019. Sentiment Polarity Detection in Azerbaijani Social News Articles. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 703–710, Varna, Bulgaria. INCOMA Ltd..