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1.16.1
This project demonstrates a lightweight, explainable social listening analysis pipeline designed to support strategic and creative decision-making in a marketing or agency context.
Using synthetic social media comments and interactions, the pipeline shows how a Creative Data / Strategy team could:
The focus is methodology and insight generation, not production-scale data engineering.
This pipeline simulates a real-world social listening and insight extraction workflow used in marketing and brand strategy. It transforms unstructured social media conversations into structured, ranked themes that highlight what consumers positively and negatively associate with each brand.
Simulates realistic user comments for two brands across social platforms, including engagement signals such as likes, replies, and shares. This mirrors the structure of data obtained from commercial social listening tools.
Standardizes raw text by lowercasing, removing emojis, URLs, punctuation, and formatting noise to ensure consistent and machine-readable input.
Assigns each mention a sentiment polarity (positive, negative, neutral) along with a sentiment intensity score to capture emotional strength.
Transforms each mention into a numerical representation that captures meaning and context, enabling comparison based on semantic similarity rather than keywords.
Groups mentions into themes based on similarity of meaning, allowing differently worded opinions about the same topic to be clustered together.
Extracts the most distinctive keywords per cluster to generate human-readable theme labels.
Outputs top positive and negative themes per brand Produces a ranked list of the most impactful positive and negative themes for each brand, enabling quick comparison and insight generation.
This project simulates a real-world social listening and insight extraction pipeline used in marketing, strategy, and creative analytics. The goal is to transform large volumes of unstructured social media conversations into clear, ranked themes that reveal what people genuinely like or dislike about each brand.
Rather than relying on keywords alone, the pipeline focuses on meaning, emotion, and engagement.
The pipeline begins by generating synthetic but realistic social media mentions for two competing brands. Each mention represents a user post or comment and includes:
This step mimics the structure of real social listening data when direct access to platforms or paid tools (e.g. Brandwatch, Talkwalker) is not available. The purpose is to test methodology and logic, not to claim real-world truth.
Raw social media text is noisy and inconsistent. Before analysis, each mention is cleaned to ensure consistency and machine readability.
This includes:
This step ensures that different ways of writing the same idea (e.g. “FAST delivery!!!” vs “fast delivery”) are treated as the same signal, not separate ones.
Each cleaned mention is analyzed to determine emotional polarity and intensity.
The model assigns:
This allows the pipeline to distinguish between:
Sentiment intensity becomes a key input when ranking themes later.
To understand meaning rather than keywords, each mention is converted into a semantic embedding.
A semantic embedding represents a sentence as a numerical vector that captures:
This allows the system to recognize that differently worded sentences such as “Shipping arrived the next day” and “Delivery was insanely fast” are semantically similar, even though they share few words.
This step is the foundation for meaning-based clustering.
Using semantic embeddings, the pipeline groups mentions into themes based on similarity of meaning.
Instead of clustering by keywords, it clusters by:
Typical themes that emerge include:
Each cluster represents a shared conversation topic, not a predefined category.
Once clusters are formed, they are automatically labeled using TF-IDF keyword extraction.
This step identifies the most distinctive words within each cluster compared to the rest of the dataset and uses them to generate short, human-readable theme labels (e.g. “Fast Delivery”, “Poor Support”, “Good In-Store Advice”).
This makes the output interpretable for non-technical stakeholders.
Each theme is ranked using a combination of:
This ensures that:
The pipeline outputs:
The result is a clear, ranked view of what matters most to consumers, suitable for strategic insight, creative briefing, or campaign direction.
This project uses simulated data, which means:
In real-world applications, insights must be validated using actual consumer-generated content.
Semantic clustering relies on statistical density:
With only a few hundred records:
Meaning-based models require scale to be reliable.
Small datasets:
This is especially risky in marketing and strategy, where decisions impact budgets, messaging, and brand perception.
Semantic embeddings and clustering are computationally expensive.
Challenges on a typical laptop include:
In production environments, this pipeline typically runs on:
This allows:
This pipeline demonstrates how raw social conversations can be transformed into structured, ranked consumer insights using modern NLP techniques. While synthetic data and limited compute impose constraints, the architecture mirrors real-world systems used in professional social listening, brand strategy, and creative analytics.
The value lies not in the specific outputs, but in the methodology: from signals → meaning → themes → strategic insight.
The stack is intentionally lean and interpretable, suitable for strategy work and interviews.
```bash python3.10 -m venv .venv
source .venv/bin/activate
pip install –upgrade pip
pip install -r requirements.txt
python3 consumerInsight.py
1.16.1