Extended topic classification utilizing LDA and BERTopic: A call center case study on robot agents and human agents

There are two ways to know why customers call the center: from the predetermined calling reason said by the customer to a Robot Agent (RA) before service with a Human Agent (HA) or directly from the customer’s conversation with an HA during the service. Obtaining tags by telling the call reason is easy, but customers can choose the wrong service operation at a non-negligible rate. So, this study used the data from 20,000 Turkish phone conversations with a HA at an inbound call center in the electronic products sector, which are handled for topic extraction with Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BERTopic) topic modeling. First, the customer speeches converted to text received from the system were passed through cleaning and editing typos. Then, the models were created, and the topic extraction process was performed. LDA and BERTopic algorithms were evaluated by comparing the machine learning technology results of the call center with HA and RA. The topics covered were used for classification with Light Gradient Boosting Machine (LGBM) linear Support Vector Machines (SVM), Long Short Term Memory (LSTM), and Logistic Regression (LR). The classification and statistical test results showed that LDA is more successful than the guided BERTopic algorithm. In addition, LDA-based classification was also more successful than RA-based classification. Although LDA-based LSTM and LR algorithms were superior to others, the best performance according to accuracy score belongs to LDA-based LSTM.