Work

Mapping UX Friction: A Neural Network Approach to Analyzing Real Time Navigation Sentiment

Year: 2026

Type: article

Abstract: ABSTRACT In the context of emotional cartography, this study introduces a neural network approach to classify user sentiment from speech transcriptions collected via Think Aloud protocols. While traditional metrics capture what users do, verbal protocols reveal how they feel and think during navigation. However, analyzing such qualitative data at scale remains a bottleneck. To address this, we trained a neural network using a novel combination of a curated, domain‐specific dataset ( n = 3052) and large‐scale app store reviews. A field experiment was conducted to empirically validate the model's ability to classify immediate, in situ emotions. The results demonstrate that supplementing generic training data with domain‐specific examples significantly improves accuracy up to a saturation point. Furthermore, by spatializing these sentiments, the study maps “UX Friction” identifying specific geographic coordinates at which interface challenges induce cognitive load. This work validates a scalable, low‐cost computational pipeline for evaluating digital maps, bridging the gap between qualitative user insights and quantitative spatial analysis.

Source: Transactions in GIS

DOI: 10.1111/tgis.70193

Authors: Vinicius Bergmann Martins, Silvana Philippi Camboim, Marcio Augusto Reolon Schmidt

Institutions: Universidade Federal do Pampa, Universidade Federal do Paraná, Universidade Federal de Uberlândia

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: bronze

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico