Can we use Bert for topic modeling?

Can we use Bert for topic modeling?

We use BERT for this purpose as it extracts different embeddings based on the context of the word. Not only that, there are many pre-trained models available ready to be used. Install the package with pip install sentence-transformers before generating the document embeddings.

What is topic modeling used for?

Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic.

What do you need to know about bertopic topic modeling?

BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. It even supports visualizations similar to LDAvis!

How to create a topic Representation in Bert?

In order to create a topic representation, we take the top 20 words per topic based on their c-TF-IDF scores. The higher the score, the more representative it should be of its topic as the score is a proxy of information density. We can use topic_sizes to view how frequent certain topics are:

How to use Bert as an embedding vector?

Bert for sentence embedding vector. Concatenated both LDA and Bert vectors with a weight hyperparameter to balance the relative importance of information from each source. Used autoencoder to learn a lower dimensional latent space representation of the concatenated vector.

Are there any multi lingual models in Bert?

The package has several multi-lingual models available for you to use. NOTE: Since transformer models have a token limit, you might run into some errors when inputting large documents. In that case, you could consider splitting documents into paragraphs. 3. Clustering