📝 Topic Modelling for Literature Reviews

Uncovering Themes in Academic Literature with Python

Literature reviews are essential but time-consuming. Modern NLP offers a shortcut: topic modelling. This article walks you through how topic modelling has evolved — from classical probabilistic models like LDA to cutting-edge neural approaches like BERTopic — and how they can help researchers summarize vast academic corpora.

📚 Why Topic Modelling for Literature Reviews?

When dealing with hundreds or thousands of academic papers, manually identifying recurring themes is inefficient. Topic modelling automates this process by uncovering latent themes — topics — within large text collections. This makes it invaluable for:

  • ✅ Rapid literature mapping
  • ✅ Trend identification
  • ✅ Systematic review support
  • ✅ Research gap detection

Let's walk through how topic modelling in Python has evolved to tackle these challenges.

🕰️ Timeline of Topic Modelling Approaches in Python

1. Latent Dirichlet Allocation (LDA) — 2003 - Still Popular

  • Library: gensim
  • Math: Probabilistic generative model, Bayesian inference
  • Key Idea: Documents are mixtures of topics; topics are mixtures of words.

Example:

from gensim import corpora, models

texts = [["data", "mining", "machine", "learning"], ["natural", "language", "processing"]]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

lda = models.LdaModel(corpus, num_topics=2, id2word=dictionary, passes=15)
topics = lda.print_topics()
print(topics)

Strengths: Interpretable, mature ecosystem
Limitations: Assumes a bag-of-words model, struggles with short texts, static topic structure

2. Non-negative Matrix Factorization (NMF) — 2014+

  • Library: scikit-learn
  • Math: Linear algebra-based, matrix decomposition
  • Key Idea: Decomposes the document-term matrix into interpretable parts — topics.

Example:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF

docs = ["Machine learning is fun", "Natural language processing is part of AI"]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)

nmf = NMF(n_components=2, random_state=1)
nmf.fit(X)

for idx, topic in enumerate(nmf.components_):
    print(f"Topic {idx}: {[vectorizer.get_feature_names_out()[i] for i in topic.argsort()[-3:]]}")

Strengths: Fast, works well with TF-IDF
Limitations: Less probabilistic rigor, assumes single topic dominance per document

3. Neural Topic Models — 2018+

  • Libraries: torch, tensorflow with custom models
  • Math: Variational Autoencoders, Neural Variational Inference
  • Key Idea: Learns topic distributions using neural networks for improved flexibility.

Example libraries like ProdLDA and ETM explore these approaches, though less plug-and-play than LDA/NMF.

Strengths: Captures complex patterns, potential for dynamic topic modelling
Limitations: Requires more data, expertise, and compute

4. BERTopic — 2020 - Modern Go-To for Many

  • Library: BERTopic
  • Math: Sentence embeddings (BERT-like models) + clustering (HDBSCAN) + class-based TF-IDF
  • Key Idea: Uses powerful transformer-based embeddings for semantic understanding, clusters them into topics.

Example:

from bertopic import BERTopic

docs = ["Natural language processing is a fascinating field.", 
        "Machine learning enables topic modelling.",
        "BERT models revolutionized NLP."]

topic_model = BERTopic()
topics, _ = topic_model.fit_transform(docs)

print(topic_model.get_topic_info())

Strengths: Handles short texts, rich semantic capture, interactive visualizations
Limitations: Requires pre-trained models, less interpretable mathematically

🔬 Comparative Summary

Feature LDA NMF Neural Models BERTopic
Multiple Topics per Doc Yes Limited Yes Yes
Underlying Math Bayesian, generative Linear algebra Neural networks Embeddings + clustering
Handles Short Texts Well No Sometimes Varies Yes
Interpretability High (word lists) Moderate Moderate High (keywords + embeddings)
Scalability Good Good Moderate Moderate to Good
Python Ecosystem Maturity Very mature Mature Emerging Rapidly growing
Best For Classic text corpora Simple use cases Advanced modelling Modern NLP tasks

📈 Choosing the Right Tool for Literature Reviews

  • Quick prototyping with interpretable topics? Start with LDA or NMF.
  • Complex or short academic texts? BERTopic shines, thanks to semantic embeddings.
  • Research-focused, experimental setups? Explore Neural Topic Models.

🎯 Final Thoughts

Topic modelling has come a long way — from bag-of-words probabilistic models to neural, contextual approaches. For literature reviews, these tools help distill knowledge efficiently, providing researchers with thematic maps of vast scholarly landscapes.

Whether you're a computational linguist, data scientist, or researcher, Python's evolving ecosystem makes topic modelling more accessible than ever.

Further Reading:

Have you used topic modelling in your research? Share your experience or questions in the comments!