signalDEV Community2026-10-05
Building for a Friend: A Local LLM Sentiment Analyzer
A developer built a local LLM sentiment analyzer using Python, Streamlit, and Ollama, running Llama 3.2 models (default 3b, optional 1b) entirely on the user's machine. It uploads CSV files, selects a text column, and returns sentiment with custom labels, explanations, filtering, data-quality checks, SQLite history, and export. Keeping data local avoids third-party API calls and per-analysis costs, and the model can be swapped without redesign.
- for who
- People who need to analyze large volumes of text feedback without sending data to external APIs
- why now
- Llama 3.2's new release enables private, cost-free local sentiment analysis right now.
- what changes
- They can process private reviews or feedback locally, eliminating privacy concerns and API bills while maintaining control over the model.
- to do
- Use the GitHub project to run your own sentiment analysis on CSV data with Ollama and a local Llama 3.2 model.
key points
- Built with Streamlit, Scikit-LLM, and scikit-ollama connecting to local Ollama
- Default llama3.2:3b model, also supports llama3.2:1b
- Features custom labels, explanations, filtering, SQLite history, and export
#local LLM#sentiment analysis#Streamlit#open source
score
score 8 out of 10. 0-10: how dense the facts are, multiplied by how much you can do with them after reading. 8+ means the topic's evidence bar is met: benchmarks and availability for a new model, amount and investors for a funding round, revenue figures for a solo-money story. Below 5 an item does not enter the digest. A press release scores 3 or less, a reprint loses 2, anything older than 14 days loses 1, a headline that misleads loses 3.
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