Reddit MCP NLP Analysis Pipeline

An MCP pipeline for Reddit data collection, NLP analysis, visualization, and Knowledge Graph generation, orchestrated by Claude.

Reddit API · MCP · Transformers · NLP · Neo4j · Python

Overview

This project connects Claude to a set of MCP servers that, starting from a simple topic (e.g. economy), handle:

• fetching relevant Reddit posts;

• cleaning and preparing the text;

• sentiment analysis and toxicity detection;

• descriptive statistics and cross-subreddit comparisons;

• charts and word clouds;

• optionally, building a Knowledge Graph in Neo4j.

Each stage is handled by an independent MCP server: Claude calls them in sequence, but each one can also be used on its own.

Related MCP server: Reddit MCP Server

How it works

flowchart TD

    A["User provides a topic"] --> B["Claude"]

    B --> C["Reddit Fetcher MCP"]
    C --> D["Reddit API"]
    D --> E["Raw Reddit Dataset"]

    E --> F["Text Analyzer MCP"]

    F --> G["Sentiment Analysis"]
    F --> H["Toxicity Detection"]
    F --> I["Text Processing"]

    G --> J["Analyzed Dataset"]
    H --> J
    I --> J

    J --> K["Visualizer MCP"]
    J --> L["Knowledge Graph MCP"]

    K --> M["Charts"]
    K --> N["Word Clouds"]
    K --> O["Subreddit Analysis"]

    L --> P["Neo4j"]

The MCP servers

The project is split into four specialized MCP servers.

Reddit Fetcher – retrieves posts for a given topic: searches Reddit, extracts text and metadata, saves results as CSV, merges different datasets, and keeps track of what's already saved locally.

Text Analyzer – does the actual NLP work: sentiment analysis, toxicity detection, language detection, word-frequency stats, descriptive statistics, extraction of positive/negative/toxic examples. The output dataset is enriched with fields like:

sentiment_score
toxic_lab
toxic_score
is_toxic

Visualizer – turns the analyzed data into visual output: sentiment distribution, posts per subreddit, average sentiment per subreddit, word clouds (including one for the toxic subset only).

Knowledge Graph – final, optional step: loads the analyzed dataset into Neo4j, modeling relationships between posts, subreddits, and sentiment class:

Post
 │
 ├── POSTED_IN ────────> Subreddit
 │
 └── HAS_SENTIMENT ────> SentimentClass

Example — topic "economy"

To test the pipeline we analyzed Reddit posts related to economy. Some of the generated outputs below.

Activity by subreddit

Which subreddits contributed the most posts to the dataset.

Sentiment distribution

Each post gets a sentiment score; here's the distribution across the whole dataset.

Sentiment by subreddit

Comparing average sentiment across the most active communities.

What's being discussed

Word cloud generated from the analyzed discussion, for a quick sense of recurring terms.

Toxic content

Posts classified as potentially toxic are isolated and analyzed separately. Here's the word cloud of the most common terms in that subset.

Example usage

Just ask something like:

Analyze the Reddit discussion around the economy.

and Claude orchestrates the MCP tools through fetching → NLP analysis → visualizations and/or Knowledge Graph, with no need to set up the pipeline manually each time.

NLP models

Sentiment analysis: nlptown/bert-base-multilingual-uncased-sentiment

Toxicity detection: facebook/roberta-hate-speech-dynabench-r4-target

Both via Hugging Face transformers.

Knowledge Graph

Current graph structure in Neo4j:

graph LR

    P["Post"]
    S["Subreddit"]
    C["SentimentClass"]

    P -->|"POSTED_IN"| S
    P -->|"HAS_SENTIMENT"| C

Each Post node can hold properties like id, title, sentiment_score, toxic_score, is_toxic.

Tech stack

Configuration

Before using the Reddit Fetcher, you'll need Reddit API credentials:

client_id
client_secret
user_agent
username
password

Don't publish real credentials in the repo.

Local directories used by the MCP servers need to be adapted to your own environment.

For the Knowledge Graph, you'll need a running Neo4j instance with:

NEO4J_URI
NEO4J_USER
NEO4J_PASSWORD

Reusability

The pipeline isn't tied to the economy example — the same architecture works with any topic ("artificial intelligence", "bitcoin", "climate change", etc.) with no changes needed.

Reddit → NLP → Insights