Setting Up LLM-Based Sentiment Analysis for Community Governance Proposals

On-chain governance, used by protocols like Uniswap, Compound, and Optimism, relies on community votes to enact changes. However, raw vote counts often fail to capture the nuanced sentiment, debate quality, and underlying concerns expressed in forum discussions. An LLM-based sentiment analysis system can process this unstructured text data—from platforms like Discourse and Commonwealth—to provide a more granular, real-time understanding of community alignment. This moves governance analytics beyond simple for/against metrics.

The core technical approach involves using a pre-trained Large Language Model (LLM) like GPT-4, Claude 3, or an open-source alternative such as Llama 3 or Mistral. These models are adept at natural language understanding (NLU) and can be prompted or fine-tuned to classify sentiment (positive, negative, neutral), identify key arguments, and detect sarcasm or toxicity in proposal discussions. For cost-effective, high-volume analysis, smaller models like BERT or DistilBERT fine-tuned on crypto-specific data can be highly effective.

A basic implementation pipeline involves three stages: data ingestion, sentiment processing, and insight aggregation. First, you fetch proposal discussions using platform APIs or RSS feeds. Next, you send comment text to an LLM endpoint with a structured prompt, such as "Classify the sentiment of this governance forum comment and extract the main concern: [COMMENT_TEXT]". Finally, you aggregate results per proposal to generate metrics like sentiment score, top concerns, and engagement heatmaps.

For developers, here is a conceptual Python snippet using the OpenAI API for sentiment classification:

pythonCopy
import openai
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a governance analyst. Classify sentiment and extract key points."},
        {"role": "user", "content": f"Comment: {forum_comment}"}
    ]
)
# Parse response for sentiment label and summary

This approach provides structured JSON output from unstructured debate.

Key challenges include managing API costs at scale, ensuring contextual accuracy for crypto-specific terminology, and mitigating model bias. Best practices involve caching results, using a vector database to find similar past comments for efficiency, and implementing a human review loop for contentious proposals. The output can be visualized in dashboards or fed back into Snapshot or Tally interfaces to inform voters, creating a more data-driven governance process.

To implement LLM-based sentiment analysis for governance data, you need a solid foundation in both blockchain interaction and machine learning workflows. The core technical stack comprises three layers: a data ingestion layer to fetch proposal text and metadata from DAO platforms like Snapshot, Compound Governor, or Aave, a processing layer for cleaning and structuring this data, and an analysis layer where the LLM performs the sentiment inference. You should be comfortable with Python, as it is the lingua franca for both Web3 development and ML/AI libraries. Familiarity with REST APIs and basic natural language processing (NLP) concepts is also highly beneficial.

For the data layer, you will need a reliable method to query on-chain and off-chain governance data. This typically involves using a blockchain node provider or API service like Alchemy, Infura, or The Graph for on-chain proposal state and voting data. For platforms like Snapshot, you will interact directly with its GraphQL API. Essential Python libraries for this stage include web3.py for Ethereum interaction, requests for HTTP calls, and pandas for data manipulation. Setting up a local database (e.g., PostgreSQL, SQLite) or using a cloud solution is recommended for storing historical proposals to avoid rate limits and enable batch processing.

The analysis layer centers on selecting and integrating an LLM. For prototyping, you can use OpenAI's GPT models via their API, which requires an API key and the openai Python library. For greater control, privacy, or cost-efficiency, consider running an open-source model locally using frameworks like LangChain or LlamaIndex, which simplify prompt engineering and context management. A popular choice is the Hugging Face Transformers library, which allows you to load models like Llama 3 or Mistral directly. You will need to define a clear prompt template that instructs the model to analyze sentiment (e.g., Positive, Negative, Neutral) and potentially extract key concerns or topics from the proposal text.

Before writing code, ensure your development environment is ready. Create a new Python virtual environment using venv or conda to manage dependencies. Key packages to install include web3, pandas, requests, openai (or transformers and torch for local models), and langchain. You will also need a crypto wallet with a small amount of ETH on a testnet (like Sepolia) if your data fetching involves writing transactions or interacting with certain smart contracts directly. Finally, secure your API keys and RPC URLs in environment variables using a .env file and a library like python-dotenv to avoid hardcoding sensitive credentials into your source code.

The foundation of any sentiment analysis system is high-quality, structured data. For on-chain governance, this data originates from two primary sources: discussion forums (e.g., Aave's Discourse, Uniswap's Agora) and voting platforms (e.g., Snapshot, Tally). Your pipeline's first job is to programmatically fetch this data. For forums, you'll use their public REST API. For example, to get proposals from the Aave Governance forum, you would query https://governance.aave.com/c/proposals.json. For Snapshot, you interact with its GraphQL API endpoint at https://hub.snapshot.org/graphql to fetch proposal details and votes.

Raw API responses are rarely analysis-ready. The next step is data normalization. This involves extracting and standardizing key fields into a consistent schema. For a proposal, essential fields include: proposal_id, title, description_body, author, created_at, category, and discussion_url. For Snapshot proposals, you also need start_block, end_block, choices, and scores. A robust pipeline handles pagination to collect historical data, manages API rate limits with exponential backoff, and stores the raw and normalized data in a database like PostgreSQL or a data lake (e.g., AWS S3).

Governance discussions contain significant noise: markdown formatting, code snippets, quotes, and off-topic comments. Text preprocessing is critical before feeding data to an LLM. Your pipeline should strip HTML/Markdown tags, remove URLs and user mentions, and filter out very short or non-textual posts. For the main proposal description, consider using a library like trafilatura or readability to extract the core article text from webpage clutter. This clean, structured text corpus is your input dataset for the next stage: sentiment labeling using a Large Language Model.

With clean text in hand, you can now use an LLM API (e.g., OpenAI's GPT-4, Anthropic's Claude, or a local model via Ollama) to perform the initial sentiment classification. The goal is to label each proposal or major forum post. You will craft a system prompt that defines the task, context, and output format. For example: "You are an analyst classifying sentiment in DAO governance proposals. For the following text, respond ONLY with one label: SUPPORT, OPPOSE, NEUTRAL, or MIXED. Base your judgment on the author's stance toward the proposal's core action." You then send the cleaned proposal description_body in a user prompt.

To ensure consistency and manage costs, implement batch processing and caching. Instead of calling the LLM for the same proposal repeatedly, your pipeline should check a database cache for existing sentiment labels. Process proposals in batches using an async workflow (e.g., with Python's asyncio and aiohttp) to parallelize API calls. Log all LLM inputs, outputs, and token usage for auditing and cost tracking. The final output of this pipeline is an enriched dataset where each governance item has a consistent structure and a preliminary LLM-generated sentiment label, ready for validation and analysis in the next step.

Fine-tuning adapts a pre-trained model like Llama-3.1-8B or Mistral-7B to a specific task—in this case, classifying the sentiment and intent behind governance discourse. You start by preparing a high-quality, labeled dataset. This involves scraping and annotating text from sources like Discourse forums (e.g., Uniswap, Compound), Snapshot proposal descriptions, and Telegram/Discord discussions. Each data point should be labeled with sentiment (e.g., positive, negative, neutral, mixed) and potentially intent tags like support, oppose, suggest_edit, or request_clarification. Tools like Label Studio or Argilla can streamline this annotation process.

Next, you select a base model and a fine-tuning method. For most projects, Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) are ideal. They achieve strong performance by training only a small subset of parameters, drastically reducing computational cost and memory requirements compared to full fine-tuning. You can implement this using libraries like Hugging Face's transformers and peft. The core code involves loading your tokenized dataset, applying the LoRA configuration to the model's attention layers, and training with a standard optimizer like AdamW.

Here is a simplified code snippet illustrating the setup using Hugging Face libraries:

pythonCopy
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import LoraConfig, get_peft_model
import torch

# Load model and tokenizer
model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, device_map="auto") # Use quantization for efficiency

# Configure LoRA
lora_config = LoraConfig(
    r=16,  # Rank
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"], # Target attention layers
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)

# Define training arguments (example)
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    warmup_steps=100,
    logging_steps=10,
    learning_rate=2e-4,
    fp16=True
)

After training, you must evaluate the model's performance on a held-out validation set. Key metrics include accuracy, F1-score (especially for imbalanced classes), and precision/recall. It's crucial to test on real-world examples from a recent governance cycle the model hasn't seen. For deployment, you can quantize the fine-tuned model further using bitsandbytes to GGUF format for efficient inference on consumer hardware or serve it via an API using vLLM or Text Generation Inference (TGI) for high throughput. This creates a pipeline where raw proposal text is fed in, and structured sentiment analysis is output.

The final system can tag sentiment at the proposal or comment level, track sentiment trends over a voting period, and even summarize key arguments for and against a proposal. This moves beyond simple keyword analysis ("great" = positive) to understand context—for instance, recognizing that "This proposal is a disaster for small holders" is negative, while "Averting that disaster was a great move" is positive. Integrating this analysis with on-chain voting data from Tally or Boardroom can reveal correlations between forum sentiment and final voting outcomes.

After collecting proposal feedback from sources like forums and social media, the next step is to analyze the text. We use a Large Language Model (LLM) to perform sentiment analysis, which is more nuanced than traditional keyword-based methods. An LLM can understand context, sarcasm, and complex arguments, assigning a sentiment score (e.g., -1 for strongly negative, +1 for strongly positive) and extracting key reasoning. For example, a comment like "This proposal is ambitious but the treasury allocation seems reckless" might receive a slightly negative score with extracted concerns about budget risk.

To implement this, you interact with an LLM API like OpenAI's GPT-4, Anthropic's Claude, or a local open-source model via an inference server. The core task is prompt engineering to get consistent, structured outputs. A robust prompt instructs the model to act as a governance analyst, outputting a JSON object with fields for sentiment_score, confidence, and key_themes. Using few-shot examples in the prompt dramatically improves accuracy by showing the model the desired output format and reasoning process for different types of feedback.

Here is a simplified Python example using the OpenAI API to score a single comment:

pythonCopy
import openai
import json

client = openai.OpenAI(api_key='your_key')

prompt = """Analyze the sentiment of this governance proposal comment.
Output JSON: {"sentiment_score": -1 to 1, "confidence": 0-1, "key_themes": ["theme1", "theme2"]}

Comment: 'I support the goal but the implementation timeline is unrealistic.'
"""

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.1  # Low temperature for consistent outputs
)

result = json.loads(response.choices[0].message.content)
print(f"Score: {result['sentiment_score']}, Themes: {result['key_themes']}")

This would likely return a score around 0.2 (mildly positive with reservation) and themes like ["supportive", "timeline_concern"].

Processing thousands of comments requires batching API calls and handling rate limits. For cost efficiency and scale, consider using smaller, fine-tuned models like Meta's Llama 3 or Mistral 7B deployed via services like Replicate, Together AI, or Hugging Face Inference Endpoints. The key is to maintain a validation set of manually scored comments to periodically check the model's performance and adjust prompts. Always log the raw LLM output for auditability and to retrain a classifier later.

Once individual comments are scored, you must aggregate the results to present a clear snapshot of community sentiment for a proposal. Simple aggregation involves calculating the mean sentiment score and score distribution (e.g., 60% positive, 25% neutral, 15% negative). More advanced aggregation weights comments by the poster's reputation or governance power (e.g., token holdings) if that data is available. Simultaneously, you should aggregate the extracted key_themes using techniques like term frequency-inverse document frequency (TF-IDF) or clustering to identify the most frequently cited arguments for and against the proposal.

The final output of this step is a structured sentiment report for each proposal, containing metrics like the overall sentiment score, confidence interval, breakdown of supporting/opposing arguments, and a list of representative comments for each major theme. This data object becomes the critical input for the final step: visualizing these insights in a governance dashboard, enabling token holders to digest complex community feedback at a glance before casting their vote.

After processing governance proposals with your LLM, you need a way to visualize the results. A dashboard allows stakeholders to track sentiment trends, compare proposals, and identify contentious issues at a glance. For this guide, we'll use Streamlit for the web framework and Plotly for interactive charts. This combination is ideal for rapid prototyping and deployment, requiring only a few Python scripts. You can host the dashboard locally for team review or deploy it to a service like Streamlit Community Cloud for public access.

Start by structuring your data. Your LLM analysis should output a structured JSON or DataFrame containing at least: the proposal title, a summary, the calculated sentiment score (e.g., -1 to +1), key argument categories, and timestamps. Load this data into a Pandas DataFrame. The core of the Streamlit app is a simple script. Begin with import streamlit as st and import plotly.express as px. Use st.title() to set the page header and st.dataframe() to display the raw data table for transparency.

Create the main visualizations. Use a Plotly bar chart (px.bar) to show sentiment scores across proposals, coloring bars from red (negative) to green (positive). Add a time-series line chart (px.line) to plot the average sentiment score over time, revealing trends in community mood. For deeper analysis, implement a sunburst chart or treemap (px.sunburst) to visualize how sentiment breaks down by proposal category and then by specific arguments, helping to pinpoint the root of support or dissent.

Enhance interactivity with Streamlit widgets. Add a st.selectbox filter to view data for a specific DAO or time period. Use st.slider to adjust the date range dynamically. Include st.metric components to display key summary statistics at the top of the dashboard, such as "Average Sentiment This Month" or "Most Controversial Proposal." This allows users to explore the data without needing to write queries, making the tool accessible to non-technical governance participants.

Finally, consider advanced features for production. Cache your data loading function with @st.cache_data to improve performance. You can integrate live data by connecting the dashboard directly to your database or to an API endpoint that runs your LLM analysis pipeline. For teams, adding user authentication and the ability to leave comments on specific proposals can turn the dashboard into a collaborative decision-making platform. The complete code for a basic version is typically under 100 lines, demonstrating the power of these modern Python libraries.

Deploying your sentiment analysis model requires a robust, scalable infrastructure. For a serverless approach, consider using AWS Lambda or Google Cloud Functions triggered by a webhook from your governance forum's API (e.g., Discourse or Snapshot). Containerize your model with Docker for consistent deployment across platforms like Kubernetes or AWS ECS. Ensure your pipeline includes preprocessing steps for text cleaning, tokenization, and handling the specific LLM's context window. A critical consideration is cost management; each API call to models like GPT-4 or Claude incurs a fee, so implement caching and batch processing for proposals with high comment volume.

Integration with on-chain actions is the ultimate goal. Your deployed service should output a structured sentiment score—for instance, a value from -1 (strongly negative) to +1 (strongly positive)—alongside key metrics like participation rate and confidence. This data can be written to an IPFS hash or a decentralized storage solution like Arweave for immutability. Smart contracts on the governance platform (e.g., a Compound Governor or OpenZeppelin Governor contract) can then read this hash via an oracle like Chainlink Functions or a custom relayer. This creates a verifiable link between community sentiment and proposal execution.

Maintaining and monitoring the system is an ongoing process. Implement logging and alerting for model drift, where the LLM's performance degrades over time due to changes in community language or new slang. Set up a dashboard using Grafana or a similar tool to track sentiment scores, API latency, and error rates. Regularly update your prompt engineering strategies and fine-tune smaller, open-source models (like Llama 3 or Mistral) based on historical data to reduce reliance on costly proprietary APIs. Finally, document the entire architecture and create a clear incident response plan for service outages to ensure the governance process remains transparent and reliable.

The system you've built provides a quantifiable metric for community sentiment, moving beyond anecdotal forum discussions. By analyzing proposal text and forum comments with models like Llama 3 or Mistral, you can generate sentiment scores (e.g., -1 to +1) and categorize feedback (Support, Against, Neutral, Concern). This data is crucial for DAO delegates to gauge alignment before a vote and for projects to iterate on proposals based on constructive criticism.

To enhance your analysis, consider integrating additional data sources. Pull on-chain voting data from Tally or Snapshot to correlate sentiment scores with final vote outcomes. Use the Discourse API to track sentiment evolution throughout a proposal's discussion period. For deeper insights, implement a topic modeling step (e.g., using BERTopic) to automatically cluster comments into themes like "tokenomics," "security," or "roadmap," providing context to the sentiment scores.

For production deployment, focus on robustness and automation. Containerize your pipeline with Docker and schedule it via GitHub Actions or a cron job to run automatically when new proposals are posted. Implement logging with structlog and error handling for API failures. Consider adding a caching layer (Redis) for LLM responses to manage costs and latency when analyzing repetitive data like common proposal templates.

The next logical step is building a frontend dashboard for stakeholders. Use a framework like Streamlit or Next.js to create a simple interface that displays sentiment trends, top concerns, and proposal summaries. This makes the data actionable for non-technical community members and delegates. Always document your methodology's limitations—LLMs can misinterpret sarcasm or complex technical debates—and use human review to calibrate the system initially.

Finally, explore advanced techniques to increase accuracy. Fine-tune an open-source model on a labeled dataset of historical governance discussions to better understand crypto-specific jargon and context. Implement agentic workflows where the LLM not only classifies sentiment but also summarizes key arguments for each side. As you scale, monitor for bias and ensure your system adheres to the same transparency principles as the governance it analyzes.