NarrativeMapper 0.1.4

NarrativeMapper is a text analysis pipeline that uncovers the dominant narratives and emotional tones within online communities.

NarrativeMapper

Overview

NarrativeMapper is a discourse analysis tool that extracts dominant narratives and emotional tone from online communities using:

• OpenAI Embeddings (Open AI text-embedding-3-large)

• Dimensionality reduction (UMAP)

• Density-based clustering (HDBSCAN)

• Topic + sentiment extraction (Open AI Chat Completions + Hugging Face distilbert-base-uncased-finetuned-sst-2-english)

Installation

Install via PyPI:

pip install NarrativeMapper

Output Formats

This example is based off of 1800 r/antiwork comments from the top 300 posts within the last year (Date of Writing: 2025-04-03).

The three formatter functions return the following:

format_to_dict() returns dict, useful for JSON export.

{
    "online_group_name": "r/antiwork",
    "clusters": [
        {
            "cluster": 0,
            "cluster_summary": "The core theme of this cluster revolves around the frustrations and challenges of the modern job application and interview process, highlighting issues such as discrimination, exploitative practices, and the disconnect between employers and candidates.",
            "tone": "NEGATIVE",
            "comment_count": 76
        },
        {
            "cluster": 1,
            "cluster_summary": "The core theme of this cluster revolves around the debate over low wages in the fast food industry, the impact of wage increases on business practices and pricing, and the broader implications for workers' livelihoods and economic conditions.",
            "tone": "NEGATIVE",
            "comment_count": 100
        },
        {
            "cluster": 2,
            "cluster_summary": "The cluster reflects widespread frustration and despair among younger generations regarding economic instability, unaffordable living costs, inadequate healthcare, and the perceived indifference of older generations towards their struggles.",
            "tone": "NEGATIVE",
            "comment_count": 112
        },
        {
            "cluster": 3,
            "cluster_summary": "The core theme of this cluster revolves around employee dissatisfaction with management practices, workplace exploitation, and the importance of asserting one's rights and boundaries in a toxic work environment.",
            "tone": "NEGATIVE",
            "comment_count": 464
        },
        {
            "cluster": 4,
            "cluster_summary": "The core theme of this cluster revolves around dissatisfaction with traditional work structures, advocating for reduced work hours, better work-life balance, and criticism of corporate exploitation and the lack of employee rights.",
            "tone": "NEGATIVE",
            "comment_count": 95
        },
        {
            "cluster": 5,
            "cluster_summary": "The core theme of this cluster revolves around wealth inequality, criticizing the hoarding of wealth by billionaires and the systemic issues that perpetuate economic disparity and exploitation of the working class.",
            "tone": "NEGATIVE",
            "comment_count": 95
        },
        {
            "cluster": 6,
            "cluster_summary": "The comments express strong criticism of capitalism, highlighting themes of exploitation, corporate greed, and the detrimental impact of billionaires and CEOs on workers and society.",
            "tone": "NEGATIVE",
            "comment_count": 89
        }
    ]
} 

format_by_cluster() returns pandas DataFrame with columns:

format_by_cluster columns

• online_group_name: online group name

• cluster: numeric cluster number

• cluster_summary: summary of the cluster

• comment_count: sampled textual messages per cluster

• aggregated_sentiment: net sentiment, of form 'NEGATIVE', 'POSITIVE', 'NEUTRAL'

• text: - the list of textual messages that are part of the cluster

• all_sentiments: this is a list containing dict items of the form '{'label': 'NEGATIVE', 'score': 0.9896971583366394}' for each message (sentiment calculated by distilbert-base-uncased-finetuned-sst-2-english).

CSV to show output format

format_by_text() returns pandas DataFrame with columns:

format_by_text columns

• online_group_name: online group name

• cluster: numeric cluster number

• cluster_summary: summary of the cluster

• text: the sampled textual message (this function returns all of them row by row)

• sentiment: dict item holding sentiment calculation, of the form '{'label': 'NEGATIVE', 'score': 0.9896971583366394}' (sentiment calculated by distilbert-base-uncased-finetuned-sst-2-english).

CSV to show output format

Pipeline Architecture & API Overview

Pipeline:

CSV Text Data → Embeddings → Clustering → Summarization → Formatting

Functions:

#Converts each message into a 3072-dimensional vector using OpenAI's text-embedding-3-large.
get_embeddings(file_path, chunk_size=...)

#Clusters the embeddings using UMAP (for reduction) and HDBSCAN (for density-based clustering).
cluster_embeddings(embeddings, n_components=..., n_neighbors=..., min_cluster_size=..., min_samples=...)

#Uses GPT (via Chat Completions) to label clusters and Hugging Face for sentiment analysis.
summarize_clusters(clustered_df, max_sample_size=...)

#Returns structured output as a dictionary (ideal for JSON export).
format_to_dict(summary_df)

#Returns a DataFrame where each row summarizes a cluster.
format_by_cluster(summary_df)

#Returns a DataFrame where each row is an individual comment with its sentiment and cluster label.
format_by_text(summary_df)

NarrativeMapper Class

Instance Attributes:

class NarrativeMapper:
    def __init__(self, online_group_name: str):
        self.online_group_name     # Name of the online community or data source
        self.embeddings            # List of dicts with original text + 3072-dim embedding
        self.cluster_df            # DataFrame after clustering
        self.summary_df            # DataFrame after summarization

Methods:

load_embeddings(file_path, chunk_size=500)
cluster(n_components=20, n_neighbors=20, min_cluster_size=40, min_samples=15)
summarize(max_sample_size=500)
format_by_text()
format_by_cluster()
format_to_dict()

Parameter Reference

Click to expand

• n_components: The number of dimensions UMAP reduces the embedding vectors to. Lower values simplify the data for clustering.

• n_neighbors: Influences UMAP’s balance between local and global structure. Higher values emphasize global relationships.

• min_cluster_size: In HDBSCAN, the minimum number of points required to form a cluster. Smaller values allow more granular clusters.

• min_samples: A density sensitivity parameter in HDBSCAN. Higher values make clustering more conservative.

• chunk_size (load_embeddings): Number of messages processed per API request to avoid token limits. Choose smaller values the larger your textual messages are.

• max_sample_size (summarize): Maximum number of comments sampled per cluster for summarization.

How to Use

IMPORTANT:

To use this package, you'll need an OpenAI API key. Create a .env file in your root directory (same folder where your script runs).

Inside the .env file, add your API key like this:

OPENAI_API_KEY="your-api-key-here"

The package will automatically load your key using python-dotenv. (Make sure to keep your .env file private and add it to your .gitignore if you're using Git.)

Option 1: High-Level Class-Based Interface

#initialize NarrativeMapper object
mapper = NarrativeMapper("r/antiwork")

#embeds semantic vectors
mapper.load_embeddings("path/to/your/file.csv", chunk_size=1000)

#clustering: n_components, n_neighbors are UMAP variables. min_cluser_size, min_samples are HDBSCAN variables.
mapper.cluster(n_components=20, n_neighbors=20, min_cluster_size=40, min_samples=15)

#summarize each cluster's topic and sentiment
mapper.summarize(max_sample_size=500)

#export in your preferred format
summary_dict = mapper.format_to_dict()
text_df = mapper.format_by_text()
cluster_df = mapper.format_by_cluster()

#saving DataFrames to csv
text_df.to_csv("comments_by_cluster.csv", index=False)
cluster_df.to_csv("cluster_summary.csv", index=False)

Option 2: Low-Level Functional Interface

#manual control over each step:
embeddings = get_embeddings("path/to/your/file.csv", chunk_size=1000)
cluster_df = cluster_embeddings(embeddings, n_components=20, n_neighbors=20, min_cluster_size=40, min_samples=15)
summary_df = summarize_clusters(cluster_df, max_sample_size=500)

#export/format options
summary_dict = format_to_dict(summary_df, online_group_name="r/antiwork")
text_df = format_by_text(summary_df, online_group_name="r/antiwork")
cluster_df = format_by_cluster(summary_df, online_group_name="r/antiwork")