Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM

Huseyn Hasanli, S. Rustamov
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引用次数: 16

Abstract

In the work, the roadmap of sentiment analysis of twits in Azerbaijani language has been developed. The principles of collecting, cleaning and annotating of twits for Azerbaijani language are described. Machine learning algorithms, such as Linear regression, Naïve Bayes and SVM applied to detect sentiment polarity of text based on bag of word models. Our suggested approach for data processing and classification can be easily adapted and applied to other Turkish language. Achieved results from different machine learning algorithm have been compared and defined optimal parameters for the classification of twits.
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基于Logistic回归、朴素贝叶斯和支持向量机的阿塞拜疆语情感分析
在工作中,制定了阿塞拜疆语推文情感分析路线图。介绍了阿塞拜疆语抽语的收集、清理和注释原则。基于词袋模型,采用线性回归、Naïve贝叶斯和SVM等机器学习算法检测文本的情感极性。我们建议的数据处理和分类方法可以很容易地适应并应用于其他土耳其语言。比较了不同机器学习算法的分类结果,确定了twits分类的最优参数。
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