Unsupervised ML - Topic Modeling LDA (Latent Dirichlet Allocation) Class

gistfile1.txt

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

class LDATopicModel:
    def __init__(self, n_topics=5, max_features=1000):
        self.n_topics = n_topics
        self.max_features = max_features
        self.vectorizer = CountVectorizer(max_features=self.max_features, stop_words='english')
        self.lda_model = LatentDirichletAllocation(n_components=self.n_topics, random_state=42)

    def fit_transform(self, documents):
        # Vectorize the text data
        document_term_matrix = self.vectorizer.fit_transform(documents)
        
        # Fit the LDA model
        self.lda_model.fit(document_term_matrix)
        
        return document_term_matrix, self.lda_model

    def display_topics(self, feature_names, n_top_words=10):
        for topic_idx, topic in enumerate(self.lda_model.components_):
            print(f"Topic {topic_idx + 1}:")
            print(" ".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))
            print()

# Example usage of the LDATopicModel class:
documents = ["Machine learning is a subfield of artificial intelligence.",
             "Natural language processing is used in text analysis.",
             "Deep learning models require large datasets.",
             "Topic modeling helps discover hidden themes in text data."]
lda_topic_model = LDATopicModel(n_topics=3)
document_term_matrix, lda_model = lda_topic_model.fit_transform(documents)
feature_names = lda_topic_model.vectorizer.get_feature_names_out()
lda_topic_model.display_topics(feature_names)