Topic Modeling with Latent Dirichlet Allocation (LDA)

In this example, we’ll explore how to apply Latent Dirichlet Allocation (LDA), a popular topic modeling technique, to extract underlying topics from a large corpus of text data.

What is LDA?

LDA is a generative model that treats each document as a mixture of multiple topics. Each topic is represented by a distribution over words in the vocabulary. The model learns to identify the most relevant words for each topic and assigns them probabilities based on their co-occurrence patterns in the training data.

Step 1: Preprocessing

Before applying LDA, we need to preprocess our text data. This involves:

• Tokenizing the text into individual words or phrases
• Removing stop words (common words like “the”, “and”, etc. that don’t add much value)
• Lemmatizing words to their base form (e.g., “running” becomes “run”)
• Vectorizing the text data using a dictionary of unique words and their corresponding numerical indices

Step 2: Training LDA Model

We’ll use the Gensim library in Python to train an LDA model on our preprocessed text data.

from gensim import corpora, models

# Load preprocessed text data
text_data = ...

# Create a dictionary of unique words and their indices
dictionary = corpora.Dictionary(text_data)

# Vectorize the text data using the dictionary
corpus = [dictionary.doc2bow(doc) for doc in text_data]

# Train an LDA model on the corpus
lda_model = models.LdaModel(corpus, id2word=dictionary, passes=15)

Step 3: Extracting Topics

Once the LDA model is trained, we can extract the underlying topics using the get_document_topics method.

# Get document-topic distributions for each document
doc_topic_dist = lda_model.get_document_topics(corpus)

# Create a data frame to store the topic assignments
toptopics_df = pd.DataFrame({'document': [doc['docno'] for doc in corpus], 'topic': [dist[1] for dist in doc_topic_dist]})

Step 4: Analyzing Topic Assignments

We can now analyze the topic assignments using various methods, such as:

• Visualizing the top words associated with each topic
• Calculating the distribution of topics across documents
• Identifying clusters or groups within the document-topic matrix

# Get the top 5 words for each topic
top_words = {}
for topic_id, topic_dist in lda_model.print_topics():
    words = [' '.join(w[0] if w else '') for w in topic_dist]
    top_words[topic_id] = words[:5]

# Plot a bar chart of the top 5 words for each topic
import matplotlib.pyplot as plt

plt.bar(top_words.keys(), [sum([w.count(word) for w in words]) for words in top_words.values()])
plt.xlabel('Topic ID')
plt.ylabel('Word Frequency')
plt.title('Top Words by Topic')

Example Output

The example output shows the top 5 words associated with each topic, along with their frequency counts. This can help identify patterns and relationships within the text data.

Topic 2: “Sports”

• football
• basketball
• soccer
• baseball
• hockey

Topic 1: “Technology”

• programming
• software
• computer
• hardware
• internet

By applying LDA to our text data, we can uncover underlying topics and themes that might not be immediately apparent through simple frequency analysis or keyword extraction.

Last modified on 2024-09-22