Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Interface for easier topic modelling.

A set of methods for finding an appropriate number of topics in a text collection

retrieval-augmented generation with web scraping

A sentiment analysis project using Twitter tweet data aimed at analysing the sentiment towards Ukrainian and Syrian refugees.

Top2Vec learns jointly embedded topic, document and word vectors.

Topic modelling data collection and analysis with Python for LLaMA dataset

X-Analyze : An Insightful Twitter/X User Profile Analyser

PubGraph - The scientific publication graph generator

Ignis: Iterative Topic Modelling Platform

GPU-Powered Topic Modelling

Multilingual topic labelling of news topics

Linked Data Triples Enhance Relevance Classification

Investigating biases in scientific publications

Implementation and helper scripts for the BART-TL model - https://www.aclweb.org/anthology/2021.eacl-main.121/

This app has been created by a group of students as part of a course in the Data Science Master’s Program at the University of Helsinki. The app was created for, and in collaboration with, the Research Unit for the Study of Variation, Contacts, and Change in English (VARIENG). It allows for the exploration of a corpus of historical letters.

The project provides insights for business owners to improve their businesses and recommendations for users to improve their experience with the application

This module teaches you how to design a chatbot using Amazon Lex by following the best design practices for conversational AI. You will start by learning the basics of chatbots. Then, you will use Amazon Lex to create a custom chatbot that gets the latest stock market quotes by recognizing the intent in text

This module looks at how use Amazon Connect, Lex, and Lambda to interact with a chatbot using voice. You will create a personal call center using Amazon Connect and you will learn how to connect the call center to your Lex chatbot