Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.832274 Artificial Intelligence Yapay Zeka Automatic Detection of the Topics in Customer Complaints with Artificial Intelligence https://orcid.org/0000-0003-1214-9235 İlhan Omurca Sevinç KOCAELİ ÜNİVERSİTESİ https://orcid.org/0000-0003-0658-592X Ekinci Ekin SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ https://orcid.org/0000-0003-1702-2647 Yakupoğlu Enes KOCAELİ ÜNİVERSİTESİ https://orcid.org/0000-0002-5978-9590 Arslan Emirhan KOCAELİ ÜNİVERSİTESİ https://orcid.org/0000-0002-3178-0690 Çapar Berkay KOCAELİ ÜNİVERSİTESİ 07 30 2021 9 3 268 277 11 27 2020 07 03 2021 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Today, people first make their complaints and compliments on internet about a product which they use or a company they are a customer of. Therefore, when they are going to buy a new product, they first analyze the complaints made by other users of the product. These complaints play an important role in helping people make decision of purchasing or not purchasing product. It is impossible to analyze online complaints manually due to the huge data size. However, companies are still losing a lot of time by analyzing and reading thousands of complaints one by one. In this article, online text based customer complaints are analyzed with Latent Dirichlet Allocation (LDA), GenSim LDA, Mallet LDA and Gibbs Sampling for Dirichlet Multinomial Mixture model (GSDMM) and the performances of them are compared. It is observed that GSDMM gives much more successful results than LDA. The obtained topics of the complaints are presented to users with a mobile application developed in React Native. With the developed application not only the customers will be able to see the topics of complaint from the application interface but also the companies will be able to view the distribution and statistics of the topics of complaints.

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