Samir Rustamov
2019
Sentiment Polarity Detection in Azerbaijani Social News Articles
Sevda Mammadli
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Shamsaddin Huseynov
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Huseyn Alkaramov
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Ulviyya Jafarli
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Umid Suleymanov
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Samir Rustamov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
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.
2013
Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models
Samir Rustamov
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Elshan Mustafayev
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Mark Clements
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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Co-authors
- Sevda Mammadli 1
- Shamsaddin Huseynov 1
- Huseyn Alkaramov 1
- Ulviyya Jafarli 1
- Umid Suleymanov 1
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