TY  - JOUR
T1  - Automatic Detection of the Topics in Customer Complaints with Artificial Intelligence
AU  - Ekinci, Ekin
AU  - İlhan Omurca, Sevinç
AU  - Yakupoğlu, Enes
AU  - Arslan, Emirhan
AU  - Çapar, Berkay
PY  - 2021
DA  - July
DO  - 10.17694/bajece.832274
JF  - Balkan Journal of Electrical and Computer Engineering
PB  - MUSA YILMAZ
WT  - DergiPark
SN  - 2147-284X
SP  - 268
EP  - 277
VL  - 9
IS  - 3
LA  - en
AB  - 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.
KW  - Topic modelling
KW  - latent dirichlet allocation
KW  - gibbs sampling
KW  - gibbs sampling for dirichlet multinomial mixture
KW  - natural language processing
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UR  - https://doi.org/10.17694/bajece.832274
L1  - https://dergipark.org.tr/en/download/article-file/1418368
ER  -