getsift 0.1.0

Scrape, cluster, and analyze product feedback from public channels

Sift

Scrape user feedback from public product channels, cluster complaints with ML, and generate AI-powered product insights — all from your terminal.

What It Does

• Scrapes G2, app stores, YouTube, Hacker News, GitHub issues, Product Hunt, forums, changelogs, and public exports for real user feedback about any product
• Anonymizes reviews at ingestion — no usernames stored, only a clickable link to the source
• Deduplicates feedback across sources using hash-based IDs so you never count the same review twice
• Clusters complaints and pain points using sentence embeddings + UMAP + HDBSCAN
• Analyzes each cluster with an LLM to name themes, summarize issues, and rate severity
• Compares multiple products to surface shared vs. unique pain points

How It Works

G2 / App Stores / YouTube / HN / GitHub / Forums / Exports
          │
          └──> Feedback Items ──> Dedup Filter ──> Sentence Embeddings ──> UMAP + HDBSCAN
                       (anonymized)                                      (all-MiniLM-L12-v2)

                                          ┌── Clustered Themes ──> LLM Analysis ──> Report (MD + JSON)
              Multi-Product Comparison <──┘

Install

Prerequisites: Python 3.11+ and an OpenAI-compatible LLM endpoint.

pip install getsift

Quick Start

# 1. Install
pip install getsift

# 2. Set up (creates config.yaml and .env with your API keys)
sift init

# 3. Run — launches the interactive Rich frontend
sift

That's it. sift opens an interactive terminal UI where you pick products and sources. No CLI arguments needed.

CLI Commands

# Interactive mode (default — just run sift)
sift

# First-run setup wizard (creates config.yaml + .env)
sift init

# Scripted/automation use:
sift analyze "Notion" "Obsidian" --source g2
sift scrape "Slack" --source g2 --source app_store

# Debug logging
sift analyze "Notion" --verbose

Configuration

Edit config.yaml to tune the pipeline:

Section	Key Options
sources	default_sources, disabled_sources
reddit	subreddits, max_posts, max_comments_per_post
g2	request_delay, max_pages, user_agent_rotation
app_store / play_store	product-to-app/package mappings, locale, item limits
youtube	video_ids, max_comments_per_video
github_issues	product-to-repo mappings, item limits
support_forums / changelogs	URL templates or product URL mappings
discord_exports / linkedin_comments	public/export JSON paths or URLs
clustering	embedding_model, umap_n_neighbors, hdbscan_min_cluster_size
llm	model, temperature, max_tokens
logging	level (INFO or DEBUG), format

LLM endpoint and API keys are set via .env:

LLM_API_KEY=your-key
LLM_BASE_URL=https://api.openai.com/v1
LLM_MODEL=gpt-4o-mini
YOUTUBE_API_KEY=optional-youtube-key
GITHUB_TOKEN=optional-github-token

Any OpenAI-compatible API works — OpenAI, Anthropic (via proxy), Ollama, OpenCode, etc.

Data Sources

Source	Method	Requirements
G2	Web scraping (BeautifulSoup)	None — includes User-Agent rotation and polite request delays
App Store	Apple customer reviews RSS	Product app IDs in config.yaml
Play Store	Public app details/reviews page	Product package names in config.yaml
YouTube comments	YouTube Data API	YOUTUBE_API_KEY and product video IDs
Hacker News	Algolia HN Search API	None
GitHub issues	GitHub Search API	Product repos; optional GITHUB_TOKEN
Product Hunt comments	Public product pages	Optional product slugs
Support forums	Configured public search URLs	Forum URL templates
Changelogs	Configured public changelog URLs	Product URL mappings
Discord exports	Public/exported JSON	JSON file paths or URLs
LinkedIn comments	Public/exported JSON	JSON file paths or URLs
Reddit	PRAW (official API)	Currently disabled in sources.disabled_sources until API approval

To reactivate Reddit later, remove reddit from sources.disabled_sources and add it to sources.default_sources if you want it in default runs.

Privacy: Usernames and PII are stripped at ingestion. Only the review text and a clickable source link are retained.

Output

Reports are saved to output/ in two formats:

• Markdown — human-readable with severity badges, representative quotes, and comparison tables
• JSON — machine-readable structured data for dashboards or downstream tools

Each report includes:

• Overall product insights (LLM-generated)
• Top pain points ranked by severity
• Per-cluster summaries with representative user quotes
• For multi-product runs: shared vs. unique pain points + competitive insights

Architecture

sift/
├── scrapers/          # Source adapters for public feedback channels
├── pipeline/          # Embeddings, clustering, LLM analysis, comparison, rate limiting, deduplication
├── models/            # Data classes (FeedbackItem, ClusterResult, ProductReport)
├── ui/                # Rich terminal frontend, setup wizard, interactive menus
├── config.py          # YAML + env var configuration loader
└── cli.py             # Click CLI (analyze, scrape, init commands)
tests/                 # Tests covering all modules

Running Tests

python -m pytest tests/ -v

Roadmap

• Reactivate Reddit source after API approval
• Web app with dashboard UI
• Continuous monitoring mode (track sentiment over time)
• Additional review sites (Trustpilot, Capterra)
• Slack/email alerting for new complaint spikes

License

MIT