socialtextlytics 2025.12.21131030

A new package that analyzes social media post text to extract structured insights about audience engagement patterns, such as identifying common themes in highly liked content or predicting potential

socialtextlytics

socialtextlytics is a Python package that analyzes social‑media post text and returns structured insights about audience engagement patterns. It can identify common themes in highly liked content, suggest language that may drive subscriber growth, and more—without handling media files directly.

Features

• Extracts categorized feedback from post text, comments, or descriptions.
• Uses a powerful LLM (ChatLLM7 by default) with regex‑based extraction for reliable outputs.
• Easily replace the LLM with any LangChain‑compatible model (OpenAI, Anthropic, Google, etc.).
• Simple, typed API.

Installation

pip install socialtextlytics

Quick Start

from socialtextlytics import socialtextlytics

# Example social‑media post text
user_input = """
Just launched our new feature! 🎉 10k likes already.
What do you think? #innovation #tech
"""

# Call the analyzer with default LLM (ChatLLM7)
results = socialtextlytics(user_input)

print(results)
# → ['...extracted insight strings...']

Advanced Usage – Providing Your Own LLM

If you prefer to use a different language model, pass a LangChain BaseChatModel instance:

OpenAI

from langchain_openai import ChatOpenAI
from socialtextlytics import socialtextlytics

llm = ChatOpenAI()          # configure with your OpenAI key as usual
response = socialtextlytics(user_input, llm=llm)
print(response)

Anthropic

from langchain_anthropic import ChatAnthropic
from socialtextlytics import socialtextlytics

llm = ChatAnthropic()
response = socialtextlytics(user_input, llm=llm)
print(response)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from socialtextlytics import socialtextlytics

llm = ChatGoogleGenerativeAI()
response = socialtextlytics(user_input, llm=llm)
print(response)

Parameters

Name	Type	Description
user_input	str	The text to analyze (post, comment, description, etc.).
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the package creates a default ChatLLM7 instance.
api_key	Optional[str]	API key for ChatLLM7. If not supplied, the function reads LLM7_API_KEY from the environment.

Default LLM – ChatLLM7

When no llm is given, socialtextlytics creates a ChatLLM7 instance:

from langchain_llm7 import ChatLLM7
resolved_llm = ChatLLM7(
    api_key=api_key,
    base_url="https://..."
)

ChatLLM7 is available on PyPI: https://pypi.org/project/langchain-llm7/ (link provided in the source).
The free tier’s rate limits are sufficient for typical usage. For higher limits, supply your own API key via the LLM7_API_KEY environment variable or the api_key argument.

You can obtain a free API key by registering at https://token.llm7.io/.

How It Works

1. Prompt Construction – The package builds system and human prompts (system_prompt, human_prompt).
2. LLM Call – The LLM processes the prompts and returns a raw response.
3. Regex Extraction – llmatch validates the response against a predefined regular expression (pattern) and extracts the structured data.
4. Result – A list of extracted insight strings is returned, or an empty list on failure.

Error Handling

If the LLM call fails or the response does not match the expected pattern, a RuntimeError is raised with an informative message.

Contributing & Issues

Bug reports, feature requests, and pull requests are welcome! Please open an issue at: https://github.com/chigwell/socialtextlytics/issues

License

This project is licensed under the MIT License.

Author

Eugene Evstafev
Email: [e-mail]
GitHub: https://github.com/chigwell

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Enjoy extracting insights from your social media content with socialtextlytics!