Metin Madenciliği ve Duygu Analizi Kullanarak Çevrimiçi İncelemelerden Alzheimer İlaçlarına İlişkin Kullanıcı Deneyimlerinin Değerlendirilmesi

Year 2023, Volume: 7 Issue: 2, 157 - 167, 30.12.2023

İbrahim Budak , Günay Kılıç , Arzu Organ

https://doi.org/10.33461/uybisbbd.1362821

Abstract

Metin madenciliği, yapılandırılmamış metin verilerinden yararlı kalıplar, eğilimler, modeller ve kurallar bulmaya çalışan yeni bir teknolojidir. Metin Madenciliğinde en yaygın kullanılan tekniklerden biri Duygu Analizidir. Duygu analizi, yazarın tutumunu keşfetmek için en yaygın kullanılan sınıflandırma aracıdır. Bir metin aracılığıyla yazarın tutumunun olumlu, olumsuz veya tarafsız olup olmadığını araştırır. İnternet çağında bilginin büyük bir kısmının metin olarak bulunması nedeniyle Duygu analizinin önemi ve kullanım alanları her geçen gün artmaktadır. Sosyal medyada sıklıkla kullanılan duygu analizi, kullanıcıların belirli bir konu veya ürün hakkındaki fikirlerini ortaya çıkarmak için kullanılabilir. Bu çalışmanın amacı, web sitelerindeki ilaç yorumlarını anlamlı bilgilere dönüştürmektir. Bu bilgiler kullanıcılar için karar vermede yardımcı olabilir. Bu çalışmada, 78 kullanıcının, Alzheimer ilaç yorumlarının bulunduğu bir sosyal platformdan elde edilen kişisel veriler değerlendirilmiştir. Özellikle Alzheimer ilaçlarının seçimi diğer ilaçların aksine, hasta ve hasta yakınlarının gözlemlerini birlikte değerlendirmeye imkân vermektedir. Değerlendirmeyi okuyan ve faydalım bulan 3723 kişi yorumun etkisini güçlendirmektedir. Uygulama aşamasında kullanıcı yorumları Duygu analizi ile polarite değerleri hesaplanmış ve geliştirilen formül ile Alzheimer ilaçları sıralanmıştır. Bu sayede tüketicilerin ilaçlara göre memnuniyet düzeyleri belirlenmiştir.

Keywords

Metin Madenciliği, Duygu Analizi, Sosyal Medya, İlaç İncelemeleri, Alzheimer İlaçları

Evaluating User Experiences of Alzheimer’s Drugs from Online Reviews Using Text Mining and Sentiment Analysis

Abstract

Text mining is a new technology that attempts to find useful patterns, trends, patterns and rules from unstructured text data. One of the most commonly used techniques in Text Mining is Sentiment Analysis. Sentiment analysis is the most widely used classification tool to explore an author's attitude. It explores whether the author's attitude is positive, negative or impartial by means of a text. As most of the information in the internet age is found as text, the importance and usage areas of Sentiment analysis are increasing day by day. Sentiment analysis, which is frequently used in social media, can be used to expose users' ideas about a particular topic or product. The aim of this study is to transform drug reviews on websites into meaningful information. This information can help users in decision-making. In this study, personal data obtained from a social platform with Alzheimer's drug reviews of 78 users were evaluated. In particular, the selection of Alzheimer's drugs, unlike other drugs, allows the observations of the patients and relatives of the patient to be evaluated together. The 3723 people who read the review and found it useful strengthens the effect of the comment. In the implementation phase, polarity values of user comments were calculated with Sentiment analysis and Alzheimer's drugs were ranked with the formula developed. In this way, the satisfaction levels of consumers according to the drugs were determined.

Keywords

Text Mining, Sentiment Analysis, Social Media, Drug Reviews, Alzheimer

References

• Ajibade, S. S. M., Zaidi, A., Tapales, C. P., Ngo-Hoang, D. L., Ayaz, M., Dayupay, J. P., ... & Adediran, A. O. (2022, December). Data Mining Analysis of Online Drug Reviews. In 2022 IEEE 10th Conference on Systems, Process & Control (ICSPC) (pp. 247-251). IEEE.
• Alsaqer, A. F., & Sasi, S. (2017, July). Movie Review Summarization and Sentiment Analysis Using Rapidminer. In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT) ( 329-335). IEEE.
• Bae, Y., & Lee, H. (2012). Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative Influence of Popular Twitterers. Journal of the American Society for Information Science and technology, 63(12), 2521-2535.
• Balbi, S., Misuraca, M., & Scepi, G. (2018). Combining Different Evaluation Systems on Social Media for Measuring User Satisfaction. Information Processing & Management, 54(4), 674-685.
• Bilisoly, R. (2011). Practical Text Mining with Perl. John Wiley & Sons.
• Cai, T., Zhu, Y., & Liu, Y. (2023, April). Research on Sentiment Classification of MOOC User Comments Based on Machine Learning. In 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (152-156). IEEE.
• Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (Eds.). (2017). A Practical Guide to Sentiment Analysis (Vol. 5). Cham: Springer International Publishing.
• Dragoni, M., Poria, S., & Cambria, E. (2018). OntoSenticNet: A Commonsense Ontology for Sentiment Analysis. IEEE Intelligent Systems, 33(3), 77-85. Drug, www.drugs.com, (15.05.2023).
• Duwairi, R. M., Marji, R., Sha'ban, N., & Rushaidat, S. (2014, April). Sentiment Analysis in Arabic Tweets. In 2014 5th International Conference on Information and Communication Systems (ICICS) (1-6). IEEE.
• Flynn, O. A., Murugadass, A., & Xiao, L. (2021). Attracting Attention in Online Health Forums: Studies of r/Alzheimers and r/dementia. In Diversity, Divergence, Dialogue: 16th International Conference, iConference 2021, Beijing, China, March 17–31, 2021, Proceedings, Part I 16 (377-395). Springer International Publishing.
• Gupta, G. ve Malhotra, S. (2015). Text Document Tokenization for Word Frequency Count Using RapidMiner (Taking Resume as an Example). International Journal of Computer Applications, 975, 8887, 24-26.
• He, W., Zha, S., & Li, L. (2013). Social Media Competitive Analysis and Text Mining: A case study in the Pizza İndustry. International Journal of Information Management, 33(3), 464-472.
• Healthcare Global, www.healthcareglobal.com/top-10/top-10-healthcare-websites, (15.05.2023)
• Hearst, M. (2003). What is Text Mining. SIMS, UC Berkeley.
• Liu, B. (2022). Sentiment Analysis and Opinion Mining. Springer Nature.
• Markopoulos, G., Mikros, G., Iliadi, A., & Liontos, M. (2015). Sentiment Analysis of Hotel Reviews in Greek: A Comparison of Unigram Features. In Cultural Tourism in a Digital Era: First International Conference IACuDiT, Athens, 2014 ( 373-383). Springer International Publishing.
• Rapidminer, https://rapidminer.com/, Erişim Tarihi: 09.12.2023.
• Rea, R., & Parsons, C. (2023). Readability, Accessibility, Quality, Visual Design and Content of Online Information on Dementia Medication: A Quantitative Evaluation. International Journal of Pharmacy Practice. 31(S1), i5-i6.
• Saad, E., Din, S., Jamil, R., Rustam, F., Mehmood, A., Ashraf, I., & Choi, G. S. (2021). Determining the Efficiency of Drugs under Special Conditions from Users’ Reviews on Healthcare Web Forums. IEEE Access, 9, 85721-85737. Similarweb, https://www.similarweb.com/, (15.05.2023).
• Shi, L., Sun, B., Kong, L., & Zhang, Y. (2009, October). Web forum Sentiment Analysis Based on Topics. In 2009 Ninth IEEE International Conference on Computer and Information Technology (2, 148-153). IEEE.
• Song, M., & Brook Wu, Y. F. (Eds.). (2008). Handbook of Research on Text and Web Mining Technologies. IGI Global.
• Subrahmanian, V. S., & Reforgiato, D. (2008). AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis. IEEE Intelligent Systems, 23(4), 43-50.
• Tan, A. H. (1999, April). Text Mining: The State of the Art and the Challenges. In Proceedings of the Pakdd 1999 Workshop on Knowledge Disocovery from Advanced Databases, 8, 65-70.
• Tayel, S., Reif, M., & Dengel, A. (2013). Rule-based Complaint Detection Using Rapidminer. In Conference: RCOMM, 141-149.
• Tripathi, P., Vishwakarma, S. K., & Lala, A. (2015, December). Sentiment Analysis of English Tweets using Rapidminer. In 2015 International Conference On Computational Intelligence and Communication Networks (CICN) (668–672). IEEE.
• Unnamalai, K. (2012). Sentiment Analysis of Products Using Web. Procedia Engineering, 38, 2257-2262.
• Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, 347-354