This project automates the extraction and analysis of chat patterns from the Bumble app, helping users understand messaging behavior and conversational dynamics at scale. Bumble Conversation Analysis streamlines repeated data-gathering tasks and transforms them into actionable interaction metrics for researchers, marketers, and automation engineers.
This automation tool captures in-app conversations, processes message logs, and generates structured insights. It removes the need for manual scrolling, copying, and reformatting, while providing consistent data for downstream analytics. Businesses and users benefit from clean datasets, improved efficiency, and repeatable workflows.
- Captures conversation transcript elements consistently across multiple sessions and devices.
- Applies NLP-driven tagging and scoring logic for message timing, tone classification, and engagement metrics.
- Supports both local and remote device execution for large-scale data pipelines.
- Minimizes human error and increases throughput for behavioral research and UX teams.
- Built with modular components that can be integrated into larger automation systems.
| Feature | Description |
|---|---|
| Automated Message Extraction | Navigates Bumble UI to retrieve conversations using Android automation services. |
| In-App Scrolling Logic | Handles long-thread pagination and scroll detection for complete data capture. |
| Sentiment Tagging | Applies NLP models to classify message tone and engagement. |
| Timestamp Normalization | Converts Bumble timestamps into standardized formats. |
| Participant Segmentation | Distinguishes message senders and assigns metadata. |
| Conversation Metrics | Computes word counts, response speeds, and engagement ratios. |
| Local/Remote Execution | Runs on single devices or device farms without code changes. |
| Retry & Recovery Routines | Self-heals failed steps using structured retry logic. |
| Export to JSON/CSV | Generates ready-to-consume datasets for analytics workflows. |
| Configurable Schedulers | Automates repeated conversation scans at specified intervals. |
Input or Trigger — The automation begins when a device session and account context are provided. Core Logic — Navigation routines open Bumble, scan conversations, collect messages, and apply NLP filters. Output or Action — Results are exported into structured JSON and CSV files for further analytics. Other Functionalities — Logging, proxy handling, and dynamic configuration support large-scale execution. Safety Controls — Throttling, timeouts, soft-fail recovery, and device-state validation protect workloads.
Language: Python Frameworks: FastAPI, lightweight NLP libraries Tools: Appilot, UI Automator, schedulers, structured logging Infrastructure: Optional containerized workers, queue-based orchestration
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- Researchers use it to collect large-scale chat patterns so they can analyze user engagement trends.
- UX teams use it to evaluate messaging usability so they can improve feature design.
- Marketers use it to study behavior segments so they can tailor communication strategies.
- Automation engineers use it to benchmark mobile automation flows so they can optimize reliability.
- Data scientists use it to enrich datasets with real-world conversational metadata.
Does it require root access? No, it operates using standard Android automation layers.
Can it run on multiple devices? Yes, the scheduler and worker model support horizontal scaling.
Does it modify app data? No, it only reads information displayed in the UI.
Is NLP configurable? Yes, models and thresholds are defined in the configuration layer.
Does it work without ADB? It supports ADB-less execution through Appilot when enabled.
Execution Speed: Typically 45–60 conversation scans per minute under device-farm conditions. Success Rate: Approximately 93–94% across long-running multi-device jobs with retry logic. Scalability: Supports 300–1,000 Android devices via sharded queues and horizontally scaled workers. Resource Efficiency: Average 1 vCPU and 350–450 MB RAM per worker-device pair. Error Handling: Automatic retries, exponential backoff, structured logs, anomaly alerts, and recovery workflows ensure resilience.
