Bert topic modeling. May 16, 2025 · Topic modeling is a computer-assisted research method from the field of machine learning that uncovers hidden thematic structures in large volumes of text. The article aims to explore the architecture, working and applications of BERT. Imagine you have hundreds of articles, books, or social media posts and want to figure out what core themes they cover. Moreover, I wanted to use transformer-based models such as BERT as they have shown amazing results in various NLP tasks over the last few years. BERTopic 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. . The BerTopic algorithm contains 3 stages: 1. Built with FastAPI, Sentence-BERT, ChromaDB, NetworkX, and Firebase. Sep 26, 2025 · This study examines the emotional and thematic patterns in AI-generated resume feedback using BERT-based topic modeling and transformer-based sentiment analysis under happy and gloomy emotional conditions to highlight AI’s robustness in providing balanced, emotionally stable feedback for career guidance. Aug 24, 2021 · BerTopic is a topic modeling technique that uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters. We use BERTopic to cluster and visualize topics extracted from natural language. The researchers built a collection of Nepali text samples, labeled each one with its topic, and then ran various models through this test to see which ones performed best. Sep 11, 2025 · BERT (Bidirectional Encoder Representations from Transformers) stands as an open-source machine learning framework designed for the natural language processing (NLP). BERTopic is a modern topic modeling framework that addresses many limitations of traditional approaches. BERTopic consists of 6 core modules that can be customized to suit different use cases. Developed by Maarten Grootendorst, it uses transformer-based embeddings (like BERT) to understand the semantic meaning of documents and clusters them based on their context rather than just word frequency. It also allows you to easily interpret and visualize the topics generated. I decided to focus on further developing the topic modeling technique the article was based on, namely BERTopic. BERTopic supports all kinds of topic modeling techniques: Leveraging BERT and a class-based TF-IDF to create easily interpretable topics. Dec 3, 2025 · BERTopic 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. Sep 28, 2022 · This tutorial explains how to do topic modeling with the BERT transformer using the BERTopic library in Python. BERTopic supports all kinds of topic modeling techniques: May 8, 2025 · We will dive deeper into BERTopic, a popular python library for transformer-based topic modeling, to help us process financial news faster and reveal how the trending topics change overtime. BERTopic supports all kinds of topic modeling techniques: BERTopic ¶ 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. Several topic modeling approaches exists, including Latent Dirichlet Allocation (LDA) and Non-negative matrix factorization. Embed the textual data (documents) In this step, the algorithm extracts document embeddings with BERT, or it can use any other embedding technique Nov 3, 2020 · Although topic models such as LDA and NMF have shown to be good starting points, I always felt it took quite some effort through hyperparameter tuning to create meaningful topics. BERTopic is a topic modeling technique that leverages BERT embeddings and a class-based TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. Illustration of BERT Model Use Case What is BERT? BERT (Bidirectional Encoder Representations from Transformers) leverages a transformer-based neural This work collects metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research and demonstrates that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's 1 day ago · This paper tackles that gap by testing how well existing BERT-based models handle Nepali sentences when asked to identify their topic. I am now at a point where BERTopic has gotten enough traction Jun 23, 2023 · BERTopic takes advantage of the superior language capabilities of (not yet sentient) transformer models and uses some other ML magic like UMAP and HDBSCAN to produce what is one of the most advanced techniques in language topic modeling today. About AI-powered system that automatically detects research gaps in scientific literature using semantic embeddings, time-aware topic modeling, citation graph analysis, and LLM-generated hypotheses. BERTopic is a topic modeling framework that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. 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. Topic modeling refers to the use of statistical techniques for extracting abstracts topics within the text. BERTopic supports all kinds of topic modeling techniques: BERT is a popular large language model that has become the de-facto standard for NLP tasks. sxd fdj udk znv kqy vyk uwl kmi lvo yuf tit quo azr cka xms