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- W3213802275 abstract "Conducting NLP for Turkish is a lot harder than other Latin-based languages such as English. In this study, by using text mining techniques, a pre-processing frame is conducted in which TF-IDF values are calculated in accordance with a linguistic approach on 7,731 tweets shared by 13 famous economists in Turkey, retrieved from Twitter. Then, the classification results are compared with four common machine learning methods (SVM, Naive Bayes, LR, and integration LR with SVM). The features represented by the TF-IDF are experimented in different N-grams. The findings show the success of a text classification problem is relative with the feature representation methods, and the performance superiority of SVM is better compared to other ML methods with unigram feature representation. The best results are obtained via the integration method of SVM with LR with the Acc of 82.9%. These results show that these methodologies are satisfying for the Turkish language." @default.
- W3213802275 created "2021-11-22" @default.
- W3213802275 creator A5068423800 @default.
- W3213802275 date "2022-01-01" @default.
- W3213802275 modified "2023-09-26" @default.
- W3213802275 title "Detection of Economy-Related Turkish Tweets Based on Machine Learning Approaches" @default.
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- W3213802275 doi "https://doi.org/10.4018/978-1-7998-8413-2.ch008" @default.
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