Topic Modelling in Python: Key Concepts, Implementation & Troubleshooting

Topic modelling in Python is a technique used to discover hidden themes in large collections of text. For example, by analyzing customer reviews, it can help you identify common concerns or preferences across thousands of messages. But figuring out what truly matters from massive data can be overwhelming. 

This article will guide you through the process of topic modelling in Python, helping you gain valuable insights without getting lost in the details.

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What is Topic Modelling? Methods and Key Concepts 

Let’s say you run an online store and have thousands of customer reviews pouring in every day. Sorting through them manually to find recurring themes is a headache. Here’s where topic modelling in Python steps in. 

It allows you to automatically discover patterns in large text data, making it easier to analyze and categorize feedback without spending hours reading every review. 

Handling topic modelling in Python isn’t just about running the algorithm. You need the right preprocessing steps, fine-tuning of parameters, and strategies. Here are three programs that can help you:

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Now, let's talk about how it's done. Below are some of the popular methods:

1. Latent Dirichlet Allocation (LDA)

Suppose you’re analyzing news articles on your website. LDA would group articles into topics like "Politics," "Technology," and "Sports" based on the words used in each article.

For example, categorizing customer reviews into broad themes like "Shipping," "Product Quality," and "Customer Service" without manually tagging each one.

2. Non-negative Matrix Factorization (NMF)

You run a restaurant and collect online reviews. NMF can help identify topics like "Food Taste," "Ambiance," and "Staff Service" based on the feedback.

With hotel reviews, NMF can break down the feedback into useful topics like "Cleanliness," "Location," and "Value for Money."

3. Latent Semantic Analysis (LSA)

For an e-commerce platform selling books, LSA could group customer reviews into topics like "Genre," "Author," and "Plot."

If you’re a fashion retailer, LSA can help categorize reviews into topics like "Size Fit," "Fabric Quality," and "Design."

Also Read: The Ultimate Guide to Text Mining in Data Mining – Start Here!

Before jumping into the implementation, it’s important to grasp some key concepts that form the foundation of topic modelling. 

Key Concepts in Topic Modelling

Suppose you’re trying to analyze customer reviews for your online store. You might want to identify topics like “product quality,” “shipping,” or “customer service.” Without a solid grasp of how topics are represented and how words are connected, you could easily end up with mixed or meaningless results.

Here’s where understanding key concepts comes into play. Here’s a quick rundown:

1. Topics and Terms: 

Topics are groups of related words. For instance, if you’re analyzing feedback from art gallery visitors, one topic might include words like "painting," "color," and "brushstrokes," pointing to a focus on visual elements. 

Another topic might feature "museum," "tour," and "exhibit," highlighting the experience itself.

2. Bag of Words vs. TF-IDF: 

Bag of Words simply counts word occurrences, treating them all equally. For example, "dog" and "cat" would be treated the same. But in TF-IDF, "dog" might carry more significance if it's less frequent in the dataset. 

Think of it like reviewing restaurant menus where "vegan" may matter more than "the."

3. Word Distributions: 

A topic is made up of words that appear together more often than by chance. If you’re analyzing tech product reviews, one topic might revolve around words like "battery," "charging," and "lifespan”. 

Another topic could focus on "camera," "resolution," and "quality," highlighting key product features.

4. Latent Dirichlet Allocation (LDA): 

LDA works by discovering topics within documents. In a set of academic papers on urban planning, LDA might find topics related to "sustainability," "architecture," and "transportation," each with a unique combination of words.

This helps to group articles that discuss similar themes without reading through each one.

5. Data Preprocessing: 

Clean your text before modelling. For instance, if you're analyzing product descriptions, removing common but unhelpful words like "model" or "style" can make your topics more meaningful. 

It’s like tidying up a messy closet before trying to organize your clothes by category.

Also Read: Feature Selection in Machine Learning: Techniques, Benefits, and More

Next, let's put these concepts into practice with Python to see how it all comes together. 

Implementing Topic Modelling: Analyzing Customer Feedback for Product Insights

With hundreds of reviews pouring in, manually reading through each one to spot recurring themes can be overwhelming. Our goal is to automatically identify recurring themes, such as "battery life," "design," or "customer service," in a large set of customer reviews. 

Instead of manually reading through all the reviews, we'll use topic modelling techniques like Latent Dirichlet Allocation (LDA) to group the feedback into meaningful topics. 

Let's break it down step by step:

Step 1: Setting Up the Python Environment

Before we begin, we need to install a few libraries that will help us process the text data and perform topic modelling. We’ll be using Gensim for LDA and NLTK for text preprocessing. 

pip install gensim nltk pandas matplotlib

• Gensim is a powerful library for topic modelling and document similarity.
• nltk is useful for text preprocessing like tokenization and stopword removal.
• Pandas will help us handle the data, and matplotlib will be used for visualizations.

Also Read: Box Plot Visualization With Pandas [Comprehensive Guide]

Step 2: Importing Libraries and Loading Data

First, we need to import the required libraries and load our customer reviews data. For simplicity, let's assume we have a CSV file with one column for reviews. 

import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from gensim import corpora
from gensim.models import LdaModel
import matplotlib.pyplot as plt

# Sample customer reviews data
data = {
    'reviews': [
        "The battery life is great, lasts all day long.",
        "I love the design, sleek and modern.",
        "The camera quality is amazing, very clear pictures.",
        "The battery drains too quickly under heavy use.",
        "Excellent customer service, very helpful.",
        "Design is beautiful, but I wish it was a bit lighter.",
        "Battery life is not as expected, needs improvement.",
        "Great product, but customer service can be better."
    ]
}

# Load data into a DataFrame
df = pd.DataFrame(data)

# Display first few reviews
print(df.head())

This will print the first few reviews so you can ensure the data is loaded correctly.

Also Read: Top 25 NLP Libraries for Python for Effective Text Analysis

Step 3: Text Preprocessing

Topic modelling requires clean, well-processed text. We'll perform the following steps:

1. Lowercasing: Convert all text to lowercase.
2. Tokenization: Split each review into individual words.
3. Removing Stopwords: Remove common words like "the," "is," "in," etc.
4. Token Cleaning: Remove any non-alphabetic words (numbers, punctuation). 

# Download stopwords from nltk
nltk.download('stopwords')
nltk.download('punkt')

# Prepare stopwords
stop_words = set(stopwords.words('english'))

def preprocess_reviews(text):
    # Tokenize the text
    tokens = word_tokenize(text.lower())
    # Remove stopwords and non-alphabetic words
    tokens = [word for word in tokens if word.isalpha() and word not in stop_words]
    return tokens

# Apply preprocessing to all reviews
df['processed_reviews'] = df['reviews'].apply(preprocess_reviews)

# Display the processed reviews
print(df['processed_reviews'])

This code will show the processed versions of the reviews, where stopwords and non-alphabetic words are removed.

Also Read: Text Summarization in NLP: Techniques, Algorithms, and Real-World Applications

Step 4: Preparing Data for LDA

LDA requires a bag-of-words model, where each document (review) is represented as a vector of word frequencies. We’ll create a dictionary of all words and convert the reviews into a bag-of-words format.

# Create a dictionary from the processed reviews
dictionary = corpora.Dictionary(df['processed_reviews'])

# Convert the reviews into a bag-of-words format
bow_corpus = [dictionary.doc2bow(review) for review in df['processed_reviews']]

# Display the bag-of-words representation of the first review
print(bow_corpus[0])

The output will look like this: 

[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1)]

Each tuple consists of a word ID and its frequency in the review.

Also Read: Getting Started with Data Exploration: A Beginner's Guide

Step 5: Building the LDA Model

Now we can build the LDA model using Gensim’s LdaModel function. We’ll specify the number of topics we want the model to find. 

# Build the LDA model
lda_model = LdaModel(bow_corpus, num_topics=3, id2word=dictionary, passes=15)

# Display the topics
topics = lda_model.print_topics(num_words=4)
for topic in topics:
    print(topic)

The output might look like this: 

(0, '0.053*"battery" + 0.045*"life" + 0.032*"great" + 0.027*"long"')
(1, '0.042*"design" + 0.038*"beautiful" + 0.035*"light" + 0.030*"wish"')
(2, '0.051*"customer" + 0.045*"service" + 0.034*"helpful" + 0.029*"product"')

Here, the model has found three topics, with the most significant words for each topic listed. Topic 0 focuses on "battery life," Topic 1 on "design," and Topic 2 on "customer service."

Also Read: Guide to Deploying Machine Learning Models on Heroku: Steps, Challenges, and Best Practices

Step 6: Visualizing the Results

To help interpret the topics, we can visualize the distribution of topics in the reviews. Gensim has a useful library called pyLDAvis, but for simplicity, we’ll use a basic bar chart here. 

# Count the distribution of topics in the corpus
topic_dist = [lda_model.get_document_topics(doc) for doc in bow_corpus]

# Visualize the distribution of topics
topic_counts = [max(doc, key=lambda x: x[1])[0] for doc in topic_dist]
plt.hist(topic_counts, bins=3, edgecolor='black')
plt.title('Topic Distribution in Customer Reviews')
plt.xlabel('Topic')
plt.ylabel('Number of Reviews')
plt.show()

This will generate a histogram showing how many reviews are associated with each of the topics.

Output:

Interpreting the Results

From the topics, we can see that:

• Topic 0 relates to "battery life," indicating that many customers are discussing how long the battery lasts.
• Topic 1 is about "design," suggesting that reviews are focusing on the product's appearance.
• Topic 2 points to "customer service," which is another key area of feedback.

These topics give you a clear, data-driven overview of what customers are discussing in your product reviews.

After implementing topic modelling and visualizing the results, it’s important to understand how to troubleshoot any issues that may arise. 

Troubleshooting Common Pitfalls and Tips for Success

Let’s say you're analyzing customer reviews for a new restaurant, but your topic model keeps grouping “menu options” with “service quality,” leading to unclear results. This can be frustrating when you’re trying to make sense of the data.

Here are some common issues you might run into:

1. Topics Are Too Broad or Vague

You're using topic modelling to analyze product reviews for a new smartwatch. But instead of specific topics like "battery life" or "screen quality," you end up with a generic topic like "product."

Why It Happens: This often occurs when the text is too general or when the number of topics is set too low. The model doesn’t have enough specificity to find distinct themes.

Solution:

• Increase the number of topics and test with different values to find the right balance.
• Consider fine-tuning the preprocessing step by removing overly common words or adding custom stopwords that aren’t helping in distinguishing topics.

2. Topics Are Overlapping or Mixed

While analyzing customer feedback for a new restaurant, your model mixes “menu variety” with “ambiance” in the same topic. This can make it difficult to make meaningful conclusions from the results.

Why It Happens: Topic modelling algorithms like LDA try to assign words to topics based on word co-occurrence. If certain words often appear together across many reviews, the model might group them together, even if they belong to different themes.

Solution:

• Adjust the alpha and beta hyperparameters in LDA to control the topic distribution across documents. This can help reduce topic overlap.
• Review your dataset; if your reviews are too short or don't have enough variation, try adding more data or adjusting the granularity of your topics.

3. Poor Topic Coherence

After applying topic modelling in Python, you end up with topics that just don’t make sense. For instance, one topic includes words like "good," "service," and "place," which feels too general to be actionable.

Why It Happens: This often occurs when the text data isn’t well-preprocessed or the model isn’t refined enough to capture the nuance of each topic.

Solution:

• Revisit your data preprocessing. Make sure you're removing stopwords, punctuation, and irrelevant terms.
• Use TF-IDF instead of Bag of Words for better feature weighting to focus on more meaningful words.
• Evaluate your model using a coherence score, which helps measure how meaningful the topics are. If the score is low, adjust the number of topics or model parameters.

4. Incorrect Number of Topics

You decide to run topic modelling in Python on a set of reviews for a tech conference. The output gives you 50 topics, but most of them are meaningless or redundant.

Why It Happens: The number of topics isn’t set optimally. Too many topics can result in overfitting, where the model identifies too many fine-grained topics, leading to a lot of noise.

Solution:

• Use a coherence score or perplexity to help determine the ideal number of topics. These metrics will help you find the right balance between too few and too many topics.
• Start small with 5 or 10 topics and adjust accordingly based on the results.

5. Model Running Slowly

If you're running topic modelling in Python on a large dataset (e.g., thousands of product reviews), you might notice the model takes a long time to train.

Why It Happens: Large datasets and a high number of topics can slow down the training process. The more data and topics, the longer it takes for the model to converge.

Solution:

• Consider using distributed computing (e.g., through cloud services or Dask) for faster processing.
• Reduce the dataset size for testing purposes, and once you’re happy with the results, scale up to the full dataset.

6. Overfitting or Underfitting

While analyzing customer complaints, your model produces topics that are too specific and only appear in a small subset of reviews. Or, the model produces topics that are so broad, they don’t reflect distinct themes.

Why It Happens: This happens when the model’s settings are either too strict (overfitting) or too loose (underfitting) in defining the topics.

Solution:

• If you suspect overfitting, try reducing the number of topics or adjust the alpha and beta values in LDA to make the topics more general.
• If you're underfitting, increase the number of topics and ensure you're giving the model enough data to learn meaningful patterns.

Focus on cleaning your data, experimenting with different models, and refining the number of topics to improve accuracy.

If you want to take it further, explore advanced topics like dynamic topic modelling, supervised topic modelling, and neural topic models.

Advance Your Machine Learning Skills with upGrad!

Projects like analyzing customer feedback for a new product or categorizing news articles based on themes offer you a hands-on way to apply topic modelling in Python. While these applications are useful, you might find it challenging to fine-tune models or handle large amounts of text data.

To improve, focus on experimenting with different preprocessing techniques, adjusting model parameters, and evaluating topic coherence. If you're looking to advance your skills, upGrad’s courses in data science, machine learning, and NLP can help you take your understanding of topic modelling further. 

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Reference:
https://arxiv.org/html/2409.15626v1