Replication package - Prioritizing Feature Requests Based on Mobile App User Reviews Using Deep Learning
Replication package - Prioritizing Feature Requests Based on Mobile App User Reviews Using Deep Learning FOLDER DESCRIPTION 1- App reviews preprocessing and classification 1- CLAP dataset 2- Feature request classification CLAP-BERT (code and model) 3- Feature request classification CLAP-GPT (code) 4- Kaggle dataset 5- Text preprocessing code 6- Feature request type classification (code and model). 2- The app reviews clustering and topic modeling 1- Feature names extraction + Clustering + Topic modeling , (code and outputs). 3- Prioritization factors (estimation/measurement) 1- Sentiment analysis (code and model) + sensitivity analysis 2- Prio
Resource information
- Format
- zip
- Original repository
- 19555555
- DOI
- 10.5281/zenodo.19555555
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
Replication package - Prioritizing Feature Requests Based on Mobile App User Reviews Using Deep Learning FOLDER DESCRIPTION 1- App reviews preprocessing and classification 1- CLAP dataset 2- Feature request classification CLAP-BERT (code and model) 3- Feature request classification CLAP-GPT (code) 4- Kaggle dataset 5- Text preprocessing code 6- Feature request type classification (code and model). 2- The app reviews clustering and topic modeling 1- Feature names extraction + Clustering + Topic modeling , (code and outputs). 3- Prioritization factors (estimation/measurement) 1- Sentiment analysis (code and model) + sensitivity analysis 2- Prioritization dataset (ground-truth) 3- Feature names extraction + Clustering + Topic modeling + Competitiveness + Sentiment, (code in one file) 4- Feature requests prioritization 1- Feature requests prioritization using MLP (code) + sensitivity analysis 5- Demo pipeline 1- Full pipline: Importing fine-tunned models+ Text preprocessing + Feature request classification + Feature request type classification + Sentiment analysis classification + Competitiveness extraction + Feature names extraction + Clustering + Topic modeling + Feature requests prioritization using MLP, (code)