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Polarity detection of Turkish comments on technology companies

机译:极性检测土耳其对技术公司的评论

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In this study, comments about technology brands are collected from a popular Turkish website, eksisözlük, and classified as positive or negative. Turkish text is preprocessed with different kinds of filters and then modeled with 1-gram, 2-grams and 3-grams language models. Naive Bayes (NB), Support Vector Machines (SVM) and K nearest neighbor (KNN) classifiers are applied on different configurations of preprocessing techniques, language models and linguistic attributes for comparison. We measured best F-measure as 0,696 on our test dataset.
机译:在这项研究中,有关技术品牌的评论是从土耳其一个受欢迎的网站eksisözlük收集的,分为正面或负面。土耳其语文本使用不同类型的过滤器进行预处理,然后使用1克,2克和3克语言模型进行建模。朴素贝叶斯(NB),支持向量机(SVM)和K最近邻(KNN)分类器应用于预处理技术,语言模型和语言属性的不同配置以进行比较。我们在测试数据集上测得的最佳F值为0696。

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