# A machine learning based empathy mapping framework for enhancing user experience through app review analysis

## TL;DR

This paper introduces a machine learning framework that automates empathy mapping using app reviews to improve user experience design.

## Contribution

The novel contribution is an automated empathy mapping process using BERT and LDA for scalable UX design insights.

## Key findings

- The BERT-based model achieved up to 98.61% binary accuracy in classifying app reviews.
- LDA topic modeling successfully identified user preferences and key themes from app reviews.
- The framework significantly improves the scalability and efficiency of empathy mapping in UX design.

## Abstract

The effectiveness of software applications largely depends on the user experience (UX), since it has a direct impact on user engagement and satisfaction. Empathy mapping is an important design thinking technique that organizes user perceptions into distinct categories for better understanding. However, traditional empathy mapping methods rely entirely on interviews and manual analysis which are both time-consuming and costly, thereby limiting the scalability of UX design and research. To address these challenges, this study presents an automated process for empathy mapping by analyzing user-posted app reviews. This study uses the Bidirectional Encoder Representations from Transformers (BERT) model for sentiment analysis, classifying user reviews as either positive (gain points or desires) or negative (pain points or frustrations). Latent Dirichlet Allocation (LDA) is then used to apply topic modeling to pinpoint preferences and important themes. By concentrating on gains and pains, this method automates the traditional manual and costly process of design thinking and empathy mapping, making it more scalable and efficient through data-driven insights. In training, the proposed model with several versions of BERT model, the binary accuracy improved from 78.14 to 98.61%, with precision achieving 97.82%, F1 score of 98.62%, and recall up to 99.42%. The validation accuracy also increased from 87.40 to 92.58%, with an F1 score 92.59%, precision of 92.43%, and recall of 92.75%. These accurate results indicate that the proposed model may be used by user experience design teams, which will help them improve and streamline UX design while also assisting developers in promptly receiving user feedback.