Öz
Bu çalışmada Twitter kullanıcılarının İyi tarım, organik tarım ve sürdürülebilir tarım hakkındaki duygu ve düşünceleri duygu analizi tekniğinden yararlanılarak analiz edilmiştir. Bu amaçla, tarımla ilgili üç farklı hashtag grubunda toplam 15984 tweet metin madenciliği tekniği ile değerlendirilmiştir. Yapılan duygu analizi sonucunda, tweet atan bireylerin bu tarım teknikleri hakkındaki duygu ve düşünceleri arasında anlamlı farklılıkların bulunduğu gözlenmiştir. Twitter kullanıcıları için en popüler ve en güvenilir tarım uygulamasının İyi Tarım uygulaması olduğu görülmüştür. Sonuç olarak, bütün tweetlere ait duygu analizi sonuçları değerlendirildiğinde Twitter kullanıcıları genel olarak tarım hakkında pozitif duygu ve düşüncelere sahip olduğu belirlenmiştir.
Anahtar Kelimeler
Kaynakça
- Abiola O., Alli A.A., Tale O.A., Misra S. and Alli O.A. (2023). Sentiment analysis of COVID‑19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser. Journal of Electrical Systems and Inf Technol (2023) 10:5. https://doi.org/10.1186/s43067-023-00070-9
- Anonymous (2023). Data Preprocessing in Data Mining (2023). Retrieved from: https://www.geeksforgeeks.org/data-preprocessing-in-data-mining/
- Anonymous (2014). Project description. TextBlob: Simplified Text Processing. ttps://pypi.org/project/textblob/0.9.0/#:~:text=TextBlob%20is%20a%20Python%20(2,classification%2C%20translation%2C%20and%20more.
- Arumugam R., Shanmugamani R. (2018). Hands-On Natural Language Processing with Python. Packt Publishing Ltd. Birmingham B3 2PB, UK. ISBN 978-1-78913-949-5.
- Barzenji H.S.A. (2021). Sentiment Analysis of Twitter Texts Using Machine Learning Algorithms. Academic Platform Journal of Engineering and Science 2021; 9-3, 460-471. Doi: 10.21541/apjes.939338
- Bonta V., Kumaresh N., Janardhan N.(2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology. ISSN: 2249-0701 Vol.8 No.S2, 2019, pp. 1-6.
- Diyasa IGSM., Mandenni NMIM., Fachrurrozi MI., Pradika SI., Manab KRN., Sasmita NR. (2021). Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob. Workshop on Environmental Science, Society, and Technology (WESTECH 2020). IOP Conf. Series: Materials Science and Engineering 1125 (2021) 012034. DOI: 10.1088/1757-899X/1125/1/012034.
- Kaur C., Sharma A. (2020). Social Issues Sentiment Analysis using Python. 2020 5th International Conference on Computing, Communication and Security (ICCCS), 14-16 October 2020, Patna, India. DOI: 10.1109/ICCCS49678.2020.9277251.
- Kulkarni A., Shivananda A. (2019). Natural Language Processing Recipes, Unlocking Text Data with Machine Learning and Deep Learning using Python. ISBN-13 (pbk): 978-1-4842-4266-7 ISBN-13 (electronic): 978-1-4842-4267-4. https://doi.org/10.1007/978-1-4842-4267-4.
- Nausheen F., Begum S.H. (2018). Sentiment Analysis to Predict Election Results Using Python. Proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018), 19-20 January 2018, Coimbatore, India, (pp. 1259-1262). DOI: 10.1109/ICISC.2018.8399007.
- Sarkar D. (2016). Text analytics with Python. A Practical Real-World Approach to Gaining Actionable Insights from Your Data. pp. 49.
- Temizhan E., Mendeş M. (2021). COVID-19 Pandemisi ile İlgili Twitter Mesajlarının Metin Madenciliği Tekniği İle Değerlendirilmesi. Turkiye Klinikleri J Biostat. 2021;13(2):185-200. DOI: 10.5336/biostatic.2020-79992.
Öz
In this study, the emotions and thoughts of Twitter users about good, organic and, sustainable agriculture practices were analyzed by using sentiment analysis technique. 15984 tweets which were analyzed by this purpose. Sentiment analysis results showed that there were significant differences between the f emotions and thoughts of the tweeting about these agricultural practices. It was observed that the most popular and reliable agricultural application for Twitter the users was the Good Agriculture practice. When all results of the sentiment analysis analysis was evaluated, it was determined that the Twitter users
Anahtar Kelimeler
Kaynakça
- Abiola O., Alli A.A., Tale O.A., Misra S. and Alli O.A. (2023). Sentiment analysis of COVID‑19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser. Journal of Electrical Systems and Inf Technol (2023) 10:5. https://doi.org/10.1186/s43067-023-00070-9
- Anonymous (2023). Data Preprocessing in Data Mining (2023). Retrieved from: https://www.geeksforgeeks.org/data-preprocessing-in-data-mining/
- Anonymous (2014). Project description. TextBlob: Simplified Text Processing. ttps://pypi.org/project/textblob/0.9.0/#:~:text=TextBlob%20is%20a%20Python%20(2,classification%2C%20translation%2C%20and%20more.
- Arumugam R., Shanmugamani R. (2018). Hands-On Natural Language Processing with Python. Packt Publishing Ltd. Birmingham B3 2PB, UK. ISBN 978-1-78913-949-5.
- Barzenji H.S.A. (2021). Sentiment Analysis of Twitter Texts Using Machine Learning Algorithms. Academic Platform Journal of Engineering and Science 2021; 9-3, 460-471. Doi: 10.21541/apjes.939338
- Bonta V., Kumaresh N., Janardhan N.(2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology. ISSN: 2249-0701 Vol.8 No.S2, 2019, pp. 1-6.
- Diyasa IGSM., Mandenni NMIM., Fachrurrozi MI., Pradika SI., Manab KRN., Sasmita NR. (2021). Twitter Sentiment Analysis as an Evaluation and Service Base On Python Textblob. Workshop on Environmental Science, Society, and Technology (WESTECH 2020). IOP Conf. Series: Materials Science and Engineering 1125 (2021) 012034. DOI: 10.1088/1757-899X/1125/1/012034.
- Kaur C., Sharma A. (2020). Social Issues Sentiment Analysis using Python. 2020 5th International Conference on Computing, Communication and Security (ICCCS), 14-16 October 2020, Patna, India. DOI: 10.1109/ICCCS49678.2020.9277251.
- Kulkarni A., Shivananda A. (2019). Natural Language Processing Recipes, Unlocking Text Data with Machine Learning and Deep Learning using Python. ISBN-13 (pbk): 978-1-4842-4266-7 ISBN-13 (electronic): 978-1-4842-4267-4. https://doi.org/10.1007/978-1-4842-4267-4.
- Nausheen F., Begum S.H. (2018). Sentiment Analysis to Predict Election Results Using Python. Proceedings of the Second International Conference on Inventive Systems and Control (ICISC 2018), 19-20 January 2018, Coimbatore, India, (pp. 1259-1262). DOI: 10.1109/ICISC.2018.8399007.
- Sarkar D. (2016). Text analytics with Python. A Practical Real-World Approach to Gaining Actionable Insights from Your Data. pp. 49.
- Temizhan E., Mendeş M. (2021). COVID-19 Pandemisi ile İlgili Twitter Mesajlarının Metin Madenciliği Tekniği İle Değerlendirilmesi. Turkiye Klinikleri J Biostat. 2021;13(2):185-200. DOI: 10.5336/biostatic.2020-79992.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Ziraat Mühendisliği, Ziraat Mühendisliği (Diğer), Ziraat, Veterinerlik ve Gıda Bilimleri |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Erken Görünüm Tarihi | 26 Eylül 2023 |
| Yayımlanma Tarihi | 27 Eylül 2023 |
| Gönderilme Tarihi | 4 Mart 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 27 Sayı: 3 |
Derginin Tarandığı İndeksler
Harran Tarım ve Gıda Bilimi Dergisi, Creative Commons Atıf –Gayrı Ticari 4.0 Uluslararası (CC BY-NC 4.0) Lisansı ile lisanslanmıştır.