Enhancing Interior Design by Exploring Mindset and Preferences of Twitter (X) Users
Keywords:
Interior Architecture Style Preference, User Mindset Modeling, Twitter (X) User Data, Sentiment Analysis, and Optimized Clustering approachAbstract
Selecting an appropriate interior architectural solution often remains challenging because applicants’ underlying design mindsets are not fully captured, while designers’ stylistic orientations may unintentionally dominate decision-making. As users’ attitudes and preferences toward interior architectural styles evolve over time, ignoring these dynamics can lead to misalignment between applicants and designers, ultimately affecting design outcomes. This research aims to improve interior architecture outcomes by identifying users’ evolving design mindsets and systematically relating them to interior designers whose stylistic orientations are most compatible, before assigning an interior designer to an applicant. To this end, large-scale social media data from Twitter (X) are analyzed using an AI-based analytical approach. User-generated content is examined through sentiment analysis to capture expressed attitudes toward interior architectural styles across time, and these attitudes are represented as temporally informed user profiles. Based on these profiles, an optimized clustering strategy is employed to model both designers’ relatively stable stylistic orientations and users’ evolving design mindsets. By linking users to designers with closely aligned stylistic and cognitive patterns, the proposed approach enables more compatible designer–applicant matching. Overall, the approach supports more informed design decisions and contributes to improved interior architectural outcomes by reducing stylistic mismatch and enhancing alignment between applicants’ mindsets and designers’ design approaches.
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Copyright (c) 2025 Seyedeh Leila Omrani Khorasani (Author); Peyman Bayat (Corresponding author); Farzaneh Asadi Malek Jahan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
