Automatic Detection of the Topics in Customer Complaints with Artificial Intelligence

Authors : Sevinç İLHAN OMURCA, Ekin EKİNCİ, Enes YAKUPOĞLU, Emirhan ARSLAN, Berkay ÇAPAR

Pages : 268-277

Doi:10.17694/bajece.832274

View : 18 | Download : 12

Publication Date : 2021-07-30

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(LDA);, GenSim LDA, Mallet LDA and Gibbs Sampling for Dirichlet Multinomial Mixture model insert ignore into journalissuearticles values(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.
Keywords : Topic modelling, latent dirichlet allocation, gibbs sampling, gibbs sampling for dirichlet multinomial mixture, natural language processing