An integrated clustering and BERT framework for improved topic modeling

Area of Science:

• Natural Language Processing
• Machine Learning
• Data Mining

Background:

• Topic modeling is crucial for extracting insights from unstructured text.
• Latent Dirichlet Allocation (LDA) is a common but sometimes limited topic modeling technique.
• Clustering algorithms offer effective unsupervised information extraction.

Purpose of the Study:

• To develop a hybrid topic modeling framework integrating BERT and LDA.
• To enhance topic coherence by incorporating clustering and dimensionality reduction.
• To create a unified approach for mining meaningful topics from massive text corpora.

Main Methods:

• A hybrid model combining Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA).
• Clustering algorithms applied for topic modeling.
• Dimensionality reduction techniques (PCA, t-SNE, UMAP) to address computational complexity.
• Experimental validation on benchmark datasets.

Main Results:

• The proposed clustering-based framework using BERT and LDA demonstrates effectiveness.
• Dimensionality reduction aids in inferring more coherent topics.
• The unified approach successfully mines meaningful topics from large text collections.

Conclusions:

• The integration of clustering and dimensionality reduction significantly improves BERT-LDA topic modeling.
• This unified framework offers a robust solution for building advanced topic modeling applications.
• The approach is effective for extracting coherent topics from massive text corpora.