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.

This project explored Twitter analytics, sentiment analysis, graph analytics, classification ML models, news API-based topic modeling, and text summarization.

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 Modeling of NIPS Papers

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 is about creating intelligent modules that can understand text and their general purpose , this is a project about using a news headline to make a predictive text module using lda , tfi-df and knn.

Topic Mdeling

Implementation of Gibbs sampling. 1. Gamma-Poisson mixture model for topic modeling 2. Bernolli-Beta Mixture model