auto-econ-sentiment

v0.2.0 safe
4.0
Medium Risk

Lexical sentiment analysis pipeline for central bank and economic text data.

🤖 AI Analysis

Final verdict: SAFE

The package presents minimal risks with no network calls, shell executions, or credential harvesting activities detected. The primary concern lies in the low repository activity and lack of maintainer information.

  • Low repository activity and maintainer details missing
  • No network calls, shell executions, or credential harvesting activities detected
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on sentiment analysis without external data sources.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: The observed pattern appears to be an artifact of code formatting or display rather than actual obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The repository's low activity and lack of maintainer details raise some concerns, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4186 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 32 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 48 commits in corybaird/auto-econ-sentiment
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • in texts] self.model.eval() all_probs: list[np.ndarray] = [] for star
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with auto-econ-sentiment 
Create a web-based application using Flask or Django that analyzes the sentiment of economic news articles and speeches from central banks. The application should allow users to input URLs of news articles or directly paste text content into a form. It will then process the text through the 'auto-econ-sentiment' package to determine the overall sentiment towards the economy based on the provided text.

Key Features:
- User-friendly interface for uploading text or pasting content.
- Real-time sentiment analysis results displayed in an easy-to-understand format (e.g., positive, neutral, negative).
- Graphical representation of sentiment trends over time if multiple analyses are performed.
- Ability to save analyzed texts and their sentiments for future reference.

Steps to Build the Application:
1. Set up your development environment with Python, Flask/Django, and install the 'auto-econ-sentiment' package.
2. Design the front-end layout focusing on user interaction, ensuring it is responsive and accessible.
3. Implement back-end functionality to handle text input and integrate 'auto-econ-sentiment' for sentiment analysis.
4. Display the sentiment analysis result immediately after processing the input text.
5. Add a feature to track sentiment trends by allowing users to save analyzed texts and view them later.
6. Test the application thoroughly to ensure reliability and accuracy of sentiment analysis.
7. Deploy the application on a cloud platform like Heroku or AWS to make it accessible online.

💬 Discussion Feed

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