KoBERTopic은 BERTopic을 한국어 데이터에 적용할 수 있도록 토크나이저와 BERT를 수정한 코드입니다.

HDBSCAN Tuning for BERTopic Models

We created a topic modeling pipeline to evaluate different topic modeling algorithms, including their performance on short and long text, preprocessed and not preprocessed datasets, and with different embedding models. Finally, we summarized the results and suggested how to choose algorithms based on the task.

BERTopic 中文使用範例

Topic modeling for NYT articles.

This is my final year project "customer reviews classification and analysis system using data mining and nlp". It analyzes and then classifies the customer reviews on the basis of their fakeness, sentiments, contexts and topics discussed. The reviews are taken from various e-commerce platforms like daraz and amazon.

豆瓣平台爬虫系统+可视化信息管理及情感分析平台

Slides, Notebook and Data for Presentation: DataHour: Harnessing ML and NLP for Elevated Customer Experiences

We present our concept of a new type of Active-Learning for Deep Learning with NLP text classification and experimentally prove its performance against Random Sampling as well as its runtime performance on the Security Threat dataset from CySecAlert. These new Active Learning algorithms are based on Sentence-BERT and BERTopic clustering algorith…

Topic modelling and analysis of different UK newspapers, primarily using BERTopic

Build interactive topic modeling pipelines.

Submission for CL4HEALTH @ LREC-COLING 2024

Natural Language Processing with Twitter data

Text Mining Final Project about Twitter Topic Modeling with different models

Aspect Based Sentiment Analysis (ABSA) of Customer Reviews

Forecasting Private Capital Market using published research and patents. Project developed at Michigan State University under the guidance of Dr. Mohammed Ghassemi for JP Morgan Chase.

Kaggle Gold Medal (13th Place) Submission to Google's LLM Prompt Recovery Challenge

A content-based recommendation system for Hacker News using topic modeling (BERTopic)

BERT-based Topic Modeling on New York Times Headlines (160k rows)

A modular, automated Python pipeline to extract insights from policy documents using NLP and LLM techniques. The goal of the project is to provide policymakers, researchers, and institutions with comprehensive insights into how different organizations are managing and regulating the use of Generative AI.