Lexicon and Transformer-Based Sentiment and Emotion Analysis of Twitter Responses to the 2023 Turkey-Syria Earthquakes
Abstract
This study analyzes Twitter data to investigate public emotional responses to the earthquakes in Turkey and Syria using a combination of Transformer-based binary sentiment analysis, VADER for multi-sentiment classification, and NRCLex for emotion categorization. The dataset comprises tweet subsets ranging from 5,000 to 40,000 posts, collected using relevant earthquake-related hashtags and keywords during the immediate aftermath of the February 2023 earthquakes. The findings reveal a clear predominance of negative sentiment across all models and tweet samples. In binary classification, 47.4% of tweets expressed negative sentiment, compared to 37.0% positive and 15.6% neutral. Multi-sentiment classification using tweet subsets ranging from 5,000 to 40,000 consistently showed higher negative sentiment levels (45.6%–48.4%) than positive sentiment (34.5%–38.7%). Emotion analysis using NRCLex further confirmed this trend, identifying "negative" as the most prevalent emotion (16.70%–17.78%), followed by "fear" (14.58%–15.58%) and "anger" (12.45%–13.47%), with positive emotions like "joy" being much less frequent (3.86%–4.11%). These results highlight the substantial psychological impact of natural disasters, where negative emotions outweigh positive and neutral expressions. The insights gained have significant implications for public health and disaster management, underlining the importance of timely, targeted interventions to address emotional distress in affected populations. Future research should aim to enhance affective response analysis and support the development of tailored mental health strategies.
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Almassar, R.R., Girsang, A.S.: “Detection of traffic congestion based on twitter using convolutional neural network model”. IAES International Journal of Artificial Intelligence 11(4), 1448 (2022).
Dehghanifard, E., Dehghani, M.H.: “Evaluation and analysis of municipal solid wastes in tehraniran”. MethodsX 5, 312–321 (2018).
Ptaszynski, M., Masui, F., Fukushima, Y., Oikawa, Y., Hayakawa, H., Miyamori, Y., Takahashi, K., Kawajiri, S.: “Deep learning for information triage on twitter” Applied Sciences 11(14), 6340 (2021).
Ruz, G.A., Henr´ıquez, P.A., Mascare˜no, “A.: Sentiment analysis of twitter data during critical events through bayesian networks classifiers.” Future Generation Computer Systems 106, 92–104 (2020).
Yao, F., Wang, Y.: “Domain-specific sentiment analysis for tweets during hurricanes (dssa-h): a domain-adversarial neural-network-based approach.” Computers, Environment and Urban Systems 83, 101522 (2020).
Ragini, J.R., Anand, P.R., Bhaskar, V.: “Big data analytics for disaster response and recovery through sentiment analysis”. International Journal of Information Management 42, 13–24 (2018).
Peri˜n´an-Pascual, C., Arcas-T´unez, F.: “Detecting environmentally related problems on twitter”. Biosystems Engineering 177, 31–48 (2019).
Zhai, W., Peng, Z.-R., Yuan, F.: “Examine the effects of neighborhood equity ondisaster situational awareness: Harness machine learning and geotagged twitter data”. International Journal of Disaster Risk Reduction 48, 101611 (2020).
Yuan, F., Li, M., Liu, R.: “Understanding the evolutions of public responses using social media: Hurricane matthew case study”. International journal of disaster risk reduction 51, 101798 (2020).
Dhanya, N., Harish, U.: “Sentiment analysis of twitter data on demonetization using machine learning techniques”. In: Computational Vision and Bio Inspired Computing, pp. 227–237 (2018). Springer
Karami, A., Shah, V., Vaezi, R., Bansal, A.: “Twitter speaks: A case of national disaster situational awareness.” Journal of Information Science 46(3), 313–324 (2020)
Contreras, D., Wilkinson, S., Balan, N., James, P.: “Assessing post-disaster recov ery using sentiment analysis: The case of l’aquila, Italy”. Earthquake Spectra 38(1), 81–108 (2022).
Sufi, F.K., Khalil, I.: “Automated disaster monitoring from social media posts using ai-based location intelligence and sentiment analysis”. IEEE Transactions on Computational Social Systems (2022).
Kaur, H., Ahsaan, S.U., Alankar, B., Chang, V.:” A proposed sentiment analysis deep learning algorithm for analyzing covid-19 tweets”. Information Systems Frontiers, 1–13 (2021).
Ruan, T., Kong, Q., McBride, S.K., Sethjiwala, A., Lv, Q.: “Cross-platform analy-sis of public responses to the 2019 ridgecrest earthquake sequence on twitter and reddit”. Scientific reports 12(1), 1634 (2022).
Parimala, M., Swarna Priya, R., Praveen Kumar Reddy, M., Lal Chowdhary, C., Kumar Poluru, R., Khan, S.:” Spatiotemporal-based sentiment analysis on tweets for risk assessment of event using deep learning approach. Software”: Practice and Experience 51(3), 550–570 (2021).
Zheng, Z., Shi, H.-Z., Zhou, Y.-C., Lu, X.-Z., Lin, J.-R.: “Earthquake impact analysis based on text mining and social media analytics”. arXiv preprint arXiv:2212.06765 (2022).
Toraman, C., Kucukkaya, I.E., Ozcelik, O., Sahin, U.: “Tweets under the rubble: Detection of messages calling for help in earthquake disaster”. arXiv preprint arXiv:2302.13403 (2023).
He, W., Yuan, Q., Li, N.: “Research on the characteristics of internet public opinion and public sentiment after the sichuan earthquake based on the perspective of Weibo”. Applied Sciences 13(3), 1335 (2023).
Maceda, L.L., Llovido, J.L., Palaoag, T.D.: “Corpus analysis of earthquake related tweets through topic modelling”. International Journal of Machine Learning and Computing 7(6), 194–197 (2017).
Contreras, D., Wilkinson, S., Aktas, Y.D., Fallou, L., Bossu, R., Land`es, M.: “Intensity-based sentiment and topic analysis. the case of the 2020 aegean earthquake”. Frontiers in Built Environment 8 (2022).
Vo, B.-K.H., Collier, N.: “Twitter emotion analysis in earthquake situations”. Int. J. Comput. Linguistics Appl. 4(1), 159–173 (2013).
Bai, H., Yu, G.: “A weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via weibo text negative sentiment analysis”. Natural Hazards 83(2), 1177–1196 (2016).
Wang, Y., Taylor, J.E.: “Coupling sentiment and human mobility in natural disasters: a twitter-based study of the 2014 south napa earthquake”. Natural hazards 92, 907–925 (2018).
Mızrak, S., Ozdemir, A., Aslan, R.: “Adaptation of hurricane risk perception scale to earthquake risk perception and determining the factors affecting women’s earthquake risk perception”. Natural Hazards 109(3), 2241–2259 (2021)
Contreras, D., Wilkinson, S., Alterman, E., Herv´as, J.: “Accuracy of a pre-trained sentiment analysis (sa) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 albanian earthquake.” Natural Hazards 113(1), 403–421 (2022).
Cabezas, J., Moctezuma, D., Fern´andez-Isabel, A., Diego, I.: “Detecting emo-tional evolution on twitter during the covid-19 pandemic using text analysis”. International Journal of Environmental Research and Public Health 18(13), 6981 (2021).
Kydros, D., Argyropoulou, M., Vrana, V.: “A content and sentiment analysis of greek tweets during the pandemic”. Sustainability 13(11), 6150 (2021).
Le´on-Sandoval, E., Zareei, M., Barbosa-Santill´an, L.I., Falc´on Morales, L.E., Pareja Lora, A., Ochoa Ruiz, G., et al.: “Monitoring the emotional response to the covid-19 pandemic using sentiment analysis: a case study in Mexico”. Computational Intelligence and Neuroscience 2022 (2022).
Jabalameli, S., Xu, Y., Shetty, S.:” Spatial and sentiment analysis of public opinion toward covid-19 pandemic using twitter data: At the early stage of vaccination”. International Journal of Disaster Risk Reduction 80, 103204 (2022).
Kausar, M.A., Soosaimanickam, A., Nasar, M.: “Public sentiment analysis on twitter data during covid-19 outbreak”. International Journal of Advanced Computer Science and Applications 12(2) (2021).
Storey, V.C., O’Leary, D.E.: “Text analysis of evolving emotions and sentiments in covid-19 twitter communication”. Cognitive Computation, 1–24 (2022).
Tobita, T., Kiyota, T., Torisu, S., Cinicioglu, O., Tonuk, G., Milev, N., ... & Shiga, M. (2024). Geotechnical damage survey report on February 6, 2023 Turkey-Syria Earthquake, Turkey. Soils and Foundations, 64(3), 101463.
Ni, S., Sun, H., Somerville, P., Yuen, D. A., Milliner, C., Wang, H., ... & Cui, Y. (2023). Complexities of the Turkey-Syria doublet earthquake sequence. The Innovation, 4(3).
Rashid, A. K., & Fındık, O. (2024). Sentiment Analysis of Twitter: Turkey Earthquake 2023 Case.
Hidayat, T., & Hassan, R. (2024). Unveiling Twitter's Take: Researching# TurkeyQuake Response & Rehabilitation. Educational Administration: Theory and Practice, 30(3), 2361-2371.
Mandal, S., Alugubelly, M., & Jayabalan, M. (2023, December). Predicting Themes of Tweets on Earthquakes in Turkey & Syria for Real-Time Classification. In 2023 16th International Conference on Developments in eSystems Engineering (DeSE) (pp. 311-316). IEEE.
Alsheyab, M., Filimon, N., & Fusté-Forné, F. (2024). Digital Content Analysis of Corporate Social Response to Turkey and Syria Earthquakes. The Arab World Geographer, 27(1), 47-62.
Cinar, E. N., Abbara, A., & Yilmaz, E. (2023). Earthquakes in Turkey
and Syriacollaboration is needed to mitigate longer terms risks to health. BMJ, 38.
DOI: https://doi.org/10.31449/inf.v49i33.6499
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