Sentiment Analysis of Tweets Associated with Turkey-Syria Earthquakes 2023

The recent world consistency is based on perceptions of people rather than people themselves. These days, the search for reliable information about natural disasters and events has become prevalent on social media sites. Hence, platforms like Twitter act as an essential tool to disseminate information and mobilize support during crises. This research study aims to investigate the sentiments and emotions of the tweets in the context of the Turkey-Syria Earthquakes of 2023. The researchers collected a dataset of tweets using relevant hashtags and keywords, labelled the dataset using stacking classifier with TextBlob, Valence Aware Dictionary, and Flair for Sentiment Reasoning, using Random Forest, Logistic Regression, Decision Tree, XGBoost, and Naïve Bayes as the base estimators. The data was analyzed using various machine learning models and deep learning architectures. All the models were compared in an analysis, thus it may be said that the Convolutional Neural Network has the highest validation accuracy followed by Support Vector Classifier and Naive Bayesian achieving the lowest, i.e., 98.1%, 97.7%, and 84.7%, respectively. The study also found that the most common emotions are ‘unemotional’ and ‘disgust’ in the tweets using stacking classifiers with TextBlob, Valence Aware Dictionary, and Wordnet. The studies reviewed in this literature review demonstrate the effectiveness of machine learning algorithms and features for sentiment analysis on current affairs and highlight the importance of considering the linguistic and cultural context of the text. However, there are still challenges to be addressed, such as dealing with noisy and biased data, adapting to different languages and domains, and handling context-dependent sentiment expressions. In addition, further research is required to improve the accuracy of sentiment analysis and to explore the use of other factors such as context, sarcasm, and irony.