این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Nonlinear Analysis and Applications، جلد ۱۳، شماره ۱، صفحات ۱۳۶۷-۱۳۷۳
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Sentiment analysis for covid-19 in Indonesia on Twitter with TF-IDF featured extraction and stochastic gradient descent
چکیده انگلیسی مقاله  Twitter is an information platform that can be used by any internet user. The opinions of the Twitter Netizens are still random or unclassified. The technique for classifying sentiment analysis requires an algorithm. One of the classification algorithms is Stochastic Gradient Descent (SGD). The more training data provided to the machine, the accuracy of the classification function model formed by the machine is also higher. But in making representations into numerical vectors, the dimensions of data become large due to the many features. Feature optimization needs to be done to the training data by reducing the dimensions of the training data while maintaining high model accuracy. The optimization feature used is the TF-IDF (term frequency-inverse document frequency) feature extraction. sentiment analysis using TF-IDF feature extraction and stochastic gradient descent algorithm can classify Indonesian text appropriately according to positive and negative sentiment. Classification Performance using TF-IDF feature extraction and stochastic gradient descent algorithm obtained an accuracy is 85.141%.
کلیدواژه‌های انگلیسی مقاله COVID-19, Twitter, Featured Extraction, Stochastic Gradient Descent
نویسندگان مقاله Vindi Dwi Antonio |
Department of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia

Syahril Efendi |
Department of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia

Herman Mawengkang |
Department of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_5743_ffddc5f6a3083b5d0785e42d1d39f891.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات