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

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

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

A rolling version of the Latent Dirichlet Allocation.

Search Engine that returns list of songs and lyrics matching a user's inputted mood

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

text mining classifier, clustering including pipeline, preprocessing

Practices and Tools of Open Science: Topic Modeling

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

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.

Topic model

Fuzzy Approach to LDA topic modeling

NLP projects for my internship at Greenhouse Group for the year 2021-2022.

Automatic story investigator of public perception on the mega urban infrastructure project

Exploring topic modeling techniques, sentiment analysis, and classification algorithms using the Kaggle dataset "Tripadvisor Reviews 2023"

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

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 repository contains the code and data used for my master's thesis in Digital Humanities at the University of Graz. The workflow is largely based on DARIAH Topics (https://github.com/DARIAH-DE/Topics).

Crowdsourcing Analysis of Bank Application Reviews

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.