• Determined the optimal number of clusters using:
  • Elbow Method and Silhouette Coefficient.
• Trained K-Means and visualized document groupings using PCA (2D).
• Interpreted cluster meaning through top TF-IDF features per cluster.

• Applied Latent Dirichlet Allocation (LDA) from gensim to uncover latent topics.
• Visualized results with pyLDAvis and WordCloud for intuitive exploration.
• Compared LDA topics with K-Means clusters to measure semantic coherence and interpretability.

• Clear cluster separation in 2D PCA visualization — indicating coherent document groupings.
• LDA revealed thematic patterns such as sentiment tone, discussion type, or contextual topics.
• Combining clustering + topic modeling enhanced both interpretability and insight depth.

This project empowers organizations to uncover customer needs, pain points, and emerging themes hidden in large volumes of unstructured text — such as reviews, surveys, or social media posts.

By combining clustering and topic modeling, the pipeline:

• Identifies key drivers of satisfaction or dissatisfaction
• Highlights emerging issues or trends in real time
• Provides a data-driven foundation for voice-of-the-customer analytics

Example:
A telecom company could cluster 100,000 feedback comments into major themes like network reliability, billing issues, and customer support response, allowing managers to prioritize actions with measurable impact.

Manual text analysis is slow and inconsistent.
This solution introduces automation and scalability, enabling teams to:

• Reduce manual classification effort by 60–80%
• Monitor feedback or ticket data in real time
• Ensure consistent topic categorization across multiple sources

This efficiency translates to faster decision cycles and lower analysis costs.

The model turns raw text into structured, decision-ready insights.
Business leaders can use this to:

• Align marketing and product strategy with authentic customer language
• Detect unmet needs and innovation opportunities
• Drive data-backed roadmap planning

By generating topic and cluster features, this workflow also creates reusable inputs for predictive analytics — such as churn, NPS, or sentiment prediction models.