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 (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.
Highlights
OVER RECENT years, online complaint or compliment narratives play a very important role in people's purchasing decisions
The study is continued by using Gibbs Sampling for Dirichlet Multinomial Mixture model (GSDMM) because it gives more successful results than other methods
After topic modelling with GSDMM, assigning the topic title to the documents is done with java programming on the NetBeans IDE platform
Summary
OVER RECENT years, online complaint or compliment narratives play a very important role in people's purchasing decisions. The frequently preferred online customer review receiving platforms provide a good resource to collect numerous text based complaints on numerous companies or brands. These collected text based data consider main aspects of the products which customers review about. Determining the main topics provides a good way to summarize and organize these unstructured text data for companies or customers. It is very important to detect the main topics of the text based complaints among the huge document collections by automating it. This way can carry out relationships between customers and companies correctly
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