Investigation of the Perception toward Health Workers and Teachers on Twitter Data via Sentiment Analysis in the Covid-19 Pandemic

GÜNYAKTI, Refika İmge; Bursa, Nurbanu

doi:10.18094/josc.980334

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…diye sorulduğunda; kullanılan ifadelerin hep olumlu olduğu, teşekkür ve minnettarlık ifadelerinin yer aldığı belirlenmiştir. Günyaktı ve Bursa'nın (2022) (31) çalışmasında, Twitter kullanıcılarının, sağlık çalışanları için paylaştıkları tweetlerin %61'inin pozitif duygu içerdiği, sağlık çalışanlarına destek vermek amacıyla sıkça pandemiye özgü kelimelerin yanı sıra "teşekkür" ve "minnet" konulu tweetler paylaştıkları görülmüştür. Foà ve ark.…”

Section: Discussionunclassified

The Professional Image of Nurses During the COVID-19 Pandemic: From the Patients’ Perspectives

Karakaplan,

Ulupınar

2023

JAREN

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Objectives:The aim of the study is to examine the views of patients who are hospitalized during the COVID-19 pandemic on the image of nursing. Method: The sample of the descriptive and cross-sectional study consisted of 400 inpatients in a city hospital. The data were collected online using a questionnaire and the Nursing Occupational Image Scale. The data were analyzed on the SPSS 22.0 program using percentage, frequency, mean, standard deviation, Pearson correlation, one-way ANOVA, and t-test. Results: The mean age of the participants was 43.4±15.4 years and 60.5% of the participants were female. The total mean score of the scale was 167.29±15.21, and the nursing image of the participants was at a good level. Patients are satisfied with the nursing services but think that the professional image could be better. During the pandemic, the nursing image of patients has changed positively. There is a significant difference between the perception of nursing image and age, education level, previous hospitalization experience, previous nursing service, and having a nurse family member(p<0.05). Conclusion:The patients' image perception of the nursing profession is at a good level. The COVID-19 pandemic has increased patients' awareness of the role and importance of nurses and has positively affected the professional image of nurses. It is important for nurses to own their roles and responsibilities in order to maintain and develop a positive perception of professional image.

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Section: Discussionunclassified

The Professional Image of Nurses During the COVID-19 Pandemic: From the Patients’ Perspectives

Karakaplan,

Ulupınar

2023

JAREN

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“…The purpose of another very recent study [19] is to make a sentiment analysis of the criticisms in the social media towards teachers and health workers during Covid-19 epidemic, especially from the perspective of children and parents. Turkish tweets containing the hashtags of health workers and teachers were collected by the Twitter API for the period of 11 March 2020 and 2 July 2021.…”

Section: Related Work About Sentiment Analysismentioning

confidence: 99%

Improving the Performance of Sentiment Analysis by Ensemble Hybrid Learning Algorithm With NLP And Cascaded Feature Extraction

Altınel

ŞAHİN>

2023

International Journal of Advances in Engineering and Pure Sciences

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Sentiment analysis is a challenging problem in Natural Language Processing since every language has its own character within several difficulties such as ambiguity, synonymy, negative suffixes…etc. Since words with ambiguity can have different sentiment scores depending on the meaning they have in their corresponding context, we accomplished a study on Turkish language to determine whether the polarity scores of these polysemous words may differ according to their meaning. For a word with ambiguity, we first made a polarity calculation module to calculate its polarity score. This way, we calculated the polarity scores of 100 Turkish polysemous words. Then, since negation directly affects the correct meaning of the word in the sentiment analysis, a negation handler module is also implemented. After that, we prepared a sentiment polarity corpus which consists of 159,876 Turkish words including 100 Turkish polysemous words. Actually, the main purpose of this study is to detect sentiment polarity of Turkish texts by considering and building a specialized module for polysemous words. In short, we built a system for Turkish sentiment polarity detection task including these modules: 1) Pre-processing, 2) Polarity Calculation Module, 3) Negation Handling Module, 4) Feature Generation Module, and 5) Classification Module. According to our knowledge, this is the first study which includes all of these modules in one Turkish sentiment analysis task. Finally, we conducted this corpus using an ensemble hybrid regularized learning algorithm on two self-collected Twitter-datasets. Experimental results show that the suggested approach improves the classification performance on Turkish sentiment analysis task.

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Covid-19 Dönemi̇nde Twi̇tter İle Hi̇sse Senedi̇ Fi̇yatlari Arasindaki̇ İli̇şki̇ni̇n İncelenmesi̇

Yılmaz

2024

Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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Bu çalışmada amaç Covid-19 döneminde Twitter ile hisse senedi fiyatları arasındaki ilişkiyi incelemektir. Bunun için kısıtlamaların yaşandığı 01.10.2020 ile 01.07.2021 tarih aralığında BIST Yıldız Pazar’da yer alan ve imalat-sanayi sektöründen üç şirketin (Akçansa Çimento Sanayi ve Ticaret A.Ş., Coco-Cola İçecek A.Ş., Goodyear Lastikleri T.A.Ş.) hisse senedi günlük kapanış fiyatları ve Twitter üzerinden bu şirketlerle ilgili paylaşılan Türkçe tweetlerin günlük duygu durumu (negatif ve pozitif) dikkate alınmıştır. Tweetlerin duygu analizinde Turkish Bert modelinden yararlanılmıştır. Turkish Bert modeli bir metni negatif ve pozitif olarak duygulara ayırmada %95’in üzerinde bir başarıya sahiptir. Duygu analizi sonucunda şirketler ile ilgili elde edilen negatif ve pozitif tweet sayıları ile günlük hisse senedi kapanış fiyatları arasındaki ilişki Granger nedensellik testi ile analiz edilmiştir. Analizin sonucunda AKCNS hisse senedi getirisi ile negatif tweetler arasında çift yönlü bir ilişki dolayısıyla bu değişkenlerin birbirini etkilediği saptanmıştır. AKCNS hisse senedi getirisi pozitif tweetleri etkilediği fakat pozitif tweetlerden etkilenmediği gözlenmiştir. CCOLA ve GOODY hisse senedi getirilerinin ise sadece pozitif ve negatif tweetleri etkilediği tespit edilmiştir. Çalışma, daha önce yerli literatürde Covid-19 dönemi için Twitter ile hisse senedi fiyatları arasındaki ilişki incelenmemesi bakımından yatırımcılara ve işletmelere önemli bilgiler sunmaktadır.

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Investigation of the Perception toward Health Workers and Teachers on Twitter Data via Sentiment Analysis in the Covid-19 Pandemic

Cited by 4 publications

References 15 publications

The Professional Image of Nurses During the COVID-19 Pandemic: From the Patients’ Perspectives

The Professional Image of Nurses During the COVID-19 Pandemic: From the Patients’ Perspectives

Improving the Performance of Sentiment Analysis by Ensemble Hybrid Learning Algorithm With NLP And Cascaded Feature Extraction

Covid-19 Dönemi̇nde Twi̇tter İle Hi̇sse Senedi̇ Fi̇yatlari Arasindaki̇ İli̇şki̇ni̇n İncelenmesi̇

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