🍵 Create and administrate validation tests for automated content analysis tools.

A rolling version of the Latent Dirichlet Allocation.

This repository contains the code for the Transformer-Representation Neural Topic Model (TNTM) based on the paper "Probabilistic Topic Modelling with Transformer Representations" by Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken and Thomas Kneib

PsychTopics – A Shiny App for Exploring and Analyzing Research Topics in Psychology

A Jupyter notebook on implementation of Latent Semantic Analysis (A Topic Modelling Algorithm) in python.

Determine a Prototype from a number of runs of Latent Dirichlet Allocation.

Meta-Lingo is a comprehensive desktop application designed for corpus linguistics research. Built with modern technologies (Electron + React + Python FastAPI), it provides powerful tools for multimodal corpus management, linguistic analysis, and annotation.

Practices and Tools of Open Science: Topic Modeling

Sentiment on K-12 Learning during COVID-19

This project showcases an end-to-end workflow for topic modeling and text analysis using a variety of machine learning and natural language processing techniques. The goal of this project is to extract meaningful topics from a collection of text documents, enabling insights, categorization, and understanding of the underlying themes in the data.

A small showcase for topic modeling with the tmtoolkit Python package. I use a corpus of articles from the German online news website Spiegel Online (SPON) to create a topic model for before and during the COVID-19 pandemic.

This repo offers a workflow dedicated to utilizing BERTopic for Semantic Graph-based information retrieval in nutrigenomics. It includes Jupyter notebooks on topic modeling and semantic graph creation, aimed at enhance genetic literature exploration. Ideal for genomic researchers, it simplifies the analysis of nutrition-related genetic information.

Topic Modeling of NIPS Papers

We have performed a multi-class classification task of literary poems, which will be assigned to a period. Raw data has been collected from the web and processed the in order to apply Natural Language Processing and Machine Learning tools, such as feature extraction and selection, topic modeling, text preprocessing and classification

Topic model

This project analyzes public opinions on digital transformation in Indonesia by scraping YouTube comments. It utilizes unsupervised learning for clustering and topic extraction. Various text preprocessing methods, such as TF-IDF, Word2Vec, and LDA, are applied for deeper insights.

Project developed and presented during the AI4CULTURE Hackathon on 12-20 February 2025 at KU Leuven, Belgium by the team ArcAIvision who took the third place in the hackathon.

Applied natural language processing (NLP) techniques to extract positive news for user-selected topics from online American news media. Topic modeling, classification modeling, and sentiment analysis were developed. A user interface was also created using Streamlit to output uplifting news for user-selected topics in the dataset.

Applied the LDA Algorithm on the data extracted from Wikivoyage page for each city.