Publication details
This post summarizes the peer-reviewed work cited below.
- Paper
- Evaluating Turkish BERT-based Language Models for Effective Customer Feedback Interpretation in CRM
- Authors
- Can İşcan; Muhammet Furkan Özara; Ahmet Erkan Çelik; Akhan Akbulut
- Presented at
- 2024 9th International Conference on Computer Science and Engineering (UBMK) — Antalya, Türkiye, 26–28 October 2024
- Pages
- 227–232
- Publication date
- October 26, 2024
- DOI
- 10.1109/ubmk63289.2024.10773436 — Publisher page
Customer relationship management (CRM) solutions are among the primary tools for strengthening customer bonds and increasing satisfaction. Prepared within the scope of the Next4biz R&D Center's "Customer Satisfaction Score Detection" project, this paper looks for an answer to the question of whether adding deep learning and natural language processing (NLP) methods to CRM platforms can produce more accurate satisfaction scores from customer feedback.
In the study, BERT-based language models trained for Turkish are compared on the task of classifying customer feedback as "complaint / not a complaint". Alongside sentiment analysis, the paper also addresses capturing recurring patterns in feedback through entity recognition; the authors state that these patterns can be used for product development and new customer acquisition. Among the models evaluated, ElecTRa stood out in accuracy, precision, recall and F1 score, and reached an average ROC AUC value of 0.93.
The practical benefit highlighted by the paper is the ability to monitor the performance of customer representatives through feedback flagged as complaints: by identifying the topics where errors concentrate, targeted training can be planned for representatives, and service quality can be improved through this feedback loop.
The study was presented at UBMK 2024 (9th International Conference on Computer Science and Engineering), held in Antalya on 26–28 October 2024, was published by IEEE in the conference proceedings (pp. 227–232), and received the "Best Paper" award at the conference.
Award: UBMK 2024 Best Paper.
