Unsupervised Topic Modeling and Latent Dirichlet Allocation (LDA) for Literature Discovery¶

Implementing unsupervised thematic extraction and statistical word-cluster mapping to identify core research categories across heterogeneous academic abstracts. Built using scikit-learn's expectation-maximization LDA solvers.

What is Topic Modeling?¶

Topic modeling is a way to discover the main themes or topics in a collection of documents without having labeled categories. It groups words that frequently appear together into topics, helping you understand large text collections at a high level.

This reference implementation follows standard production design: text tokenization, vocabulary vectorization using high-frequency stop-word pruning, data splitting for generalizability testing, and LDA parameter optimization.

1. Import Libraries and Prepare Data¶

Import the libraries needed for text processing. We'll build a sample corpus from research-style abstracts and convert it to a vector representation for modeling.

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

corpus = [
    "High-energy physics experiments at CERN produce large datasets used to study particle collisions and test the Standard Model.",
    "A genomics team uses CRISPR technology to map regulatory regions and understand trait heritability in Arabidopsis.",
    "Hydrology research leverages SWOT satellite observations to estimate river discharge and surface water elevation across ungauged basins.",
    "Machine learning models are being applied to integrate remote sensing data, climate model output, and field measurements for snowpack estimation.",
    "Natural language processing can support literature reviews by identifying key topics in scientific proposals and collaborative grants."
]

vectorizer = CountVectorizer(stop_words='english', min_df=1)
X = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names_out()  # TODO: what is this doing? extracting main text and not the stop words!
print('Corpus size:', len(corpus))
print('Vocabulary size:', len(feature_names))

import numpy as np import pandas as pd from sklearn.feature_extraction.text import CountVectorizer corpus = [ "High-energy physics experiments at CERN produce large datasets used to study particle collisions and test the Standard Model.", "A genomics team uses CRISPR technology to map regulatory regions and understand trait heritability in Arabidopsis.", "Hydrology research leverages SWOT satellite observations to estimate river discharge and surface water elevation across ungauged basins.", "Machine learning models are being applied to integrate remote sensing data, climate model output, and field measurements for snowpack estimation.", "Natural language processing can support literature reviews by identifying key topics in scientific proposals and collaborative grants." ] vectorizer = CountVectorizer(stop_words='english', min_df=1) X = vectorizer.fit_transform(corpus) feature_names = vectorizer.get_feature_names_out() # TODO: what is this doing? extracting main text and not the stop words! print('Corpus size:', len(corpus)) print('Vocabulary size:', len(feature_names))

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feature_names

feature_names

2. Split Dataset into Training and Testing Sets¶

For a small demonstration corpus, we can still show how to hold out text for validation. This is useful when you want to check whether topic assignments generalize to unseen documents.

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from sklearn.model_selection import train_test_split

train_texts, test_texts = train_test_split(corpus, test_size=0.2, random_state=42)

print('Training documents:', len(train_texts))
print('Testing documents:', len(test_texts))
print('Test sample:', test_texts[0])

from sklearn.model_selection import train_test_split train_texts, test_texts = train_test_split(corpus, test_size=0.2, random_state=42) print('Training documents:', len(train_texts)) print('Testing documents:', len(test_texts)) print('Test sample:', test_texts[0])

3. Train Topic Model¶

Use Latent Dirichlet Allocation (LDA) from scikit-learn to learn core themes in the corpus. LDA is a topic modeling algorithm that assumes each document is a mixture of topics and each topic is a mixture of words. It learns the topic structure by estimating which words belong to each topic and how much each topic contributes to each document. We will fit the model on the training split.

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from sklearn.decomposition import LatentDirichletAllocation

train_X = vectorizer.fit_transform(train_texts)
lda = LatentDirichletAllocation(n_components=3, random_state=42)
lda.fit(train_X)

print('LDA components shape:', lda.components_.shape)

from sklearn.decomposition import LatentDirichletAllocation train_X = vectorizer.fit_transform(train_texts) lda = LatentDirichletAllocation(n_components=3, random_state=42) lda.fit(train_X) print('LDA components shape:', lda.components_.shape)

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lda

lda

4. Inspect Topics¶

Display the top words for each topic. This is a common way to interpret topic models and evaluate whether they have captured coherent themes.

Before running the code, note that each topic is shown as a bag of high-probability words. These words are not labels, but they help you understand what the topic is about.

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def print_topics(model, feature_names, n_top_words=8):
    for topic_idx, topic in enumerate(model.components_):
        message = "Topic #{}: ".format(topic_idx + 1)
        message += " ".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]])
        print(message)

print_topics(lda, vectorizer.get_feature_names_out())

def print_topics(model, feature_names, n_top_words=8): for topic_idx, topic in enumerate(model.components_): message = "Topic #{}: ".format(topic_idx + 1) message += " ".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]) print(message) print_topics(lda, vectorizer.get_feature_names_out())