Topic Modeling with BERTopic

In today’s data-driven world, text data is everywhere — customer reviews, social media posts, articles, and more. But how do we extract meaningful insights from this vast sea of unstructured information? Enter topic modeling, an unsupervised learning technique that organizes text into themes or topics, making it easier to interpret large datasets.

In this post, we’ll explore BERTopic, a powerful tool for topic modeling, and show you how to leverage it to uncover hidden insights in your text data.

What Is BERTopic?

BERTopic is a topic modeling library in Python that uses state-of-the-art embeddings to discover clusters of similar content in text data. Unlike traditional methods such as Latent Dirichlet Allocation (LDA), BERTopic leverages transformer-based embeddings (e.g., BERT, RoBERTa) to capture the semantic meaning of text, resulting in more coherent and meaningful topics.

Why Use BERTopic?

1. Semantic Richness: By using transformer embeddings, BERTopic captures the context of words in a way traditional models cannot.
2. Dynamic Visualization: BERTopic provides rich visualizations, making it easy to explore and interpret topics.
3. Ease of Use: The library is beginner-friendly and integrates seamlessly with Python workflows.
4. Flexibility: You can fine-tune the embeddings, clustering algorithms, and topic representation for your specific use case.

Getting Started with BERTopic

1. Install BERTopic

Start by installing BERTopic and any necessary dependencies:

pip install bertopic

2. Load Your Dataset

Let’s assume you have a dataset of customer reviews. For demonstration purposes, we’ll use a small sample dataset.

reviews = [
    "The product quality is excellent and delivery was fast.",
    "Customer service was terrible and unhelpful.",
    "I love the sleek design and usability of this product.",
    "The shipping took too long, but the item was well-packaged.",
    "Great value for the price! Will buy again.",
    "Had an issue with the item, but support resolved it quickly."
]

3. Train the BERTopic Model

BERTopic makes it easy to create and train a model:

from bertopic import BERTopic

# Initialize the BERTopic model
topic_model = BERTopic()

# Fit the model to your text data
topics, probs = topic_model.fit_transform(reviews)

Here:

• topics represents the topic assigned to each document.
• probs provides the probability of each document belonging to its assigned topic.

4. Explore the Topics

Use BERTopic’s built-in tools to get a summary of the topics:

# Display the discovered topics
print(topic_model.get_topic_info())

The output will include a list of topics along with their sizes and key words. For example:

   Topic  Count       Name
0      -1      1  Outliers
1       0      4  product, quality, delivery
2       1      2  customer, service, unhelpful

5. Visualize the Topics

Visualizations are one of BERTopic’s standout features:

# Visualize the topic distributions
topic_model.visualize_topics()

You’ll get an interactive chart showing the relationships between topics, helping you better understand your data.

Advanced Features

1. Custom Embeddings: Use your own embeddings for domain-specific data.
2. Topic Reduction: Merge similar topics to refine the model:

topic_model.reduce_topics(reviews, nr_topics=2)

3. Dynamic Topics Over Time: Analyze how topics evolve with temporal data.

topic_model.visualize_barchart(top_n_topics=5)

Use Cases for BERTopic

• Customer Feedback Analysis: Discover recurring themes in customer reviews to inform product improvements.
• Social Media Monitoring: Identify trends and user sentiments from tweets or posts.
• Content Categorization: Organize large collections of articles or documents into meaningful categories.

Why BERTopic Over Traditional Methods?

Traditional methods like LDA struggle with capturing contextual meaning, often leading to less coherent topics. BERTopic, by leveraging transformer-based embeddings, overcomes these limitations and provides a richer, more accurate representation of text.

For instance, while LDA might group “customer service” and “product quality” under the same topic, BERTopic’s semantic understanding can distinguish between them based on context.

Conclusion

BERTopic is a game-changer for topic modeling, combining cutting-edge NLP techniques with user-friendly features. Whether you’re analyzing customer reviews or exploring academic papers, BERTopic simplifies the process of uncovering insights from unstructured text data.

Give BERTopic a try in your next project, and you’ll see just how powerful and intuitive topic modeling can be. Happy modeling!

Further Reading

• BERTopic Documentation
• Introduction to Topic Modeling

# Fit the model to your text data
topics, probs = topic_model.fit_transform(reviews)