Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4489
Title: | Sentiment Classification on Turkish Tweets About Covid-19 Using Lstm Network | Authors: | Çataltaş, Mustafa Üstünel, Büşra Baykan, Nurdan Akhan |
Abstract: | As Covid-19 pandemic affected everyone in various aspects, people have been expressing their opinions on these aspects mostly on social media platforms because of the pandemic. These opinions play a crucial role in understanding the sentiments towards the pandemic. In this study, Turkish tweets on Covid-19 topic were collected from March 2020 to January 2021 and labelled as positive, negative, or neutral in terms of sentiment using BERT which is a pre-trained text classifier model. Using this labelled dataset, a set of experiments were carried out with SVM, Naive Bayes, K-Nearest Neighbors, and CNN-LSTM model machine learning algorithms for binary and multi-class classification tasks. Results of these experiments have shown that CNN-LSTM model outperforms other machine learning algorithms which are used in this study in both binary classification and multi-class classification tasks. | URI: | https://doi.org/10.36306/konjes.1173939 https://search.trdizin.gov.tr/yayin/detay/1180910 https://hdl.handle.net/20.500.13091/4489 |
ISSN: | 2667-8055 |
Appears in Collections: | TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
10.36306-konjes.1173939-2644940.pdf | 1.31 MB | Adobe PDF | View/Open |
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