COVID-19 Hakkındaki Türkçe Tweetlerde LSTM Ağı Kullanılarak Duygu Sınıflandırması

Year 2023, Volume: 11 Issue: 2, 341 - 353, 01.06.2023

Mustafa Çataltaş , Büşra Üstünel , Nurdan Akhan Baykan

https://doi.org/10.36306/konjes.1173939

Abstract

Covid-19 pandemisi herkesi çeşitli yönlerden etkilediğinden, pandemi nedeniyle insanlar daha çok sosyal medya platformlarında bu yönlere ilişkin görüşlerini dile getiriyorlar. Bu görüşler, pandemiye yönelik duyguları anlamada çok önemli bir rol oynamaktadır. Bu çalışmada, 2020'den 2021'e kadar Covid-19 konulu Türkçe tweet'ler toplanmış ve önceden eğitilmiş bir metin sınıflandırıcı modeli kullanılarak duygu açısından olumlu, olumsuz veya nötr olarak etiketlenmiştir. Bu etiketli veri kümesini kullanarak, ikili ve çok sınıflı sınıflandırma görevleri için SVM, Naive Bayes, K-Nearest Neighbors ve CNN-LSTM model makine öğrenme algoritmaları için bir dizi deney gerçekleştirilmiştir.

Keywords

Duygu Analizi, Türkçe Twitter, Sınıflandırma, LSTM

SENTIMENT CLASSIFICATION ON TURKISH TWEETS ABOUT COVID-19 USING LSTM NETWORK

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.

Keywords

Sentiment analysis, Turkish Twitter, Classification, LSTM

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