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Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It encompasses various techniques and algorithms designed to process and analyze textual data, enabling machines to derive meaningful insights from unstructured text.
One of the key challenges in NLP is topic modeling, which involves uncovering hidden themes or subjects within a large collection of documents. Traditional approaches, such as Latent Dirichlet Allocation (LDA), have been widely used for topic modeling. However, these methods often struggle to capture the semantic nuances and contextual relationships present in the text.
BERTopic represents a groundbreaking advancement in topic modeling by harnessing the power of the Bidirectional Encoder Representations from the Transformers (BERT) model. BERT, developed by Google, is a state-of-the-art language model that has revolutionized various NLP tasks.
The unsupervised nature of BERTopic makes it particularly valuable for analyzing large-scale datasets where manual annotation or prior knowledge about the data is limited. By automatically extracting topics, BERTopic enables researchers, businesses, and organizations to gain valuable insights into their textual data’s underlying themes and patterns.
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Working of BERTopic
The inner workings of BERTopic can be divided into three main steps: document embedding, topic modeling, and topic visualization.
- Document embedding: The first step in BERTopic is to encode the documents into dense vector representations using a pre-trained BERT model. BERT uses a transformer architecture to encode the text, which allows it to capture contextual information about the text.
In BERTopic, the documents are first preprocessed by removing stop words, punctuation, and other noise. Then, each document is fed into the BERT model, which produces a dense vector representation for each document.
- Topic modeling: After the documents have been encoded into dense vector representations, BERTopic uses a clustering algorithm to group similar documents into topics. The clustering algorithm used in BERTopic is Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which is a powerful clustering algorithm that can handle non-linearly separable data.
HDBSCAN works by first clustering the documents into smaller clusters, or subclusters, based on their density. Then, it merges these subclusters into larger clusters based on their similarity. This process continues until all the documents have been assigned to a cluster.
- Topic Visualization: Once the documents have been clustered into topics, BERTopic uses a technique called UMAP (Uniform Manifold Approximation and Projection) to visualize the topics in a low-dimensional space. UMAP is a dimensionality reduction technique that can preserve the local structure of the data.
In BERTopic, the dense vector representations of the documents are projected onto a two-dimensional space using UMAP. Each topic is then represented by a centroid in this space, allowing us to visualize the topics and their relationships.
BERTopic vs. LDA and other topic modeling techniques
- LDA represents each document as a bag of words, where the order of the words is not considered. BERTopic, on the other hand, represents each document using a dense vector that captures the meaning of the text.
- Another difference is in the way they handle word embeddings. LDA uses a fixed set of pre-trained word embeddings, while BERTopic fine-tunes a pre-trained BERT model to encode the documents into dense vectors.
- Additionally, LDA requires the user to specify the number of topics to be extracted, whereas BERTopic automatically determines the number of topics based on the data.
- One advantage of BERTopic over LDA and other traditional topic modeling techniques is that it can better capture the nuances and complexities of language. BERT has been trained on a large text corpus and can encode the documents into dense vectors that capture their semantic meaning. BERTopic can also handle long documents, whereas LDA struggles with longer texts.
- However, one disadvantage of BERTopic is that it can be computationally expensive and require significant resources to fine-tune the BERT model and cluster the documents. LDA, on the other hand, is relatively simple and computationally efficient.
Overall, both LDA and BERTopic have advantages and disadvantages, and the choice of technique will depend on the specific needs and resources of the user.
In this blog, we introduced BERTopic, a topic modelling technique that uses the BERT language model. We have shown how to install and use BERTopic to extract topics from a dataset of news articles.
BERTopic is a powerful technique that can be used to extract topics from any text dataset, and its ability to encode documents into dense vectors makes it particularly useful for large-scale datasets.
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Drop a query if you have any questions regarding BERTopic and I will get back to you quickly.
1. What is the optimal number of topics to extract using BERTopic?
ANS: – The optimal number of topics to extract using BERTopic depends on the document collection size, the language’s complexity, and the desired level of granularity. BERTopic uses an elbow plot to help users determine the optimal number of topics.
2. Can BERTopic handle non-English languages?
ANS: – Yes, BERTopic can handle non-English languages. However, the pre-trained BERT models used by BERTopic are typically trained on English text, so the performance may not be as good for non-English languages. It is possible to fine-tune BERT models on non-English text to improve performance, but this requires additional data and resources.
3. Can BERTopic handle large document collections?
ANS: – Yes, BERTopic is designed to handle large document collections. However, the computational resources required to process large document collections can be significant, particularly if fine-tuning the BERT model. Using a GPU or distributed computing is recommended to speed up the processing time for large document collections.
WRITTEN BY Sanjay Yadav
Sanjay Yadav is working as a Research Associate - Data and AIoT at CloudThat. He has completed Bachelor of Technology and is also a Microsoft Certified Azure Data Engineer and Data Scientist Associate. His area of interest lies in Data Science and ML/AI. Apart from professional work, his interests include learning new skills and listening to music.