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Prioritized Perspectives and Concerns in Arabic Reviews of FemTech Apps: A Mixed Method of Topic Modeling and Multi-Criteria Analysis

computational-linguistics
natural-language-and-speech
social-computing
text-mining
sentiment-analysis

Abstract

Background. Female Technology (FemTech) apps are increasingly used worldwide, but perspectives from Arabic-speaking users remain underreported. Understanding user priorities in this context is critical for culturally relevant app development and health policy.

Objective. To identify and prioritize user-reported themes—both concerns and positive perspectives—in Arabic-language reviews of FemTech apps across the MENA region.

Methods. FemTech apps were systematically identified across 16 Arabic-speaking countries in Google Play and Apple App Store. Public user reviews were collected, cleaned, and normalized from dialectal Arabic to Modern Standard Arabic using a prompt-controlled workflow with manual verification. Sentiment was classified using a transformer-based Arabic model. Topics were extracted via BERTopic (AraBERTv2 + HDBSCAN), then coded by three experts with substantial inter-rater agreement. To prioritize themes, four criteria (topic frequency, user importance strength, recency, and app-version spread) were weighted using Analytic Hierarchy Process (AHP) and ranked with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

Results. After screening, 51 apps (37 Android, 14 iOS) and 26,151 Arabic reviews were included. Sentiment aligned with star ratings, with positive reviews predominating at 5 stars and negatives at 1 star. Topic reduction yielded 50 topics grouped into 8 themes. AHP assigned the highest weights to frequency (0.380) and user importance (0.344). TOPSIS ranked widespread user approval and gratitude as the highest theme, followed by easy-to-use tracking/interface. Poor Arabic language support was the top concern. Other mid-ranked themes included long-term use & recommendation and useful content to support reproductive health. Issues with login/ads/paywall and prediction accuracy ranked lower due to moderate frequency and spread.

Conclusions. Arabic reviews show predominantly positive perspectives, yet language support and monetization remain significant concerns. The proposed pipeline demonstrates a reproducible way to mine app-store reviews in underrepresented languages, offering actionable priorities for developers, researchers, and policymakers.

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