topic-modelling

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

Top2Vec learns jointly embedded topic, document and word vectors.

Interface for easier topic modelling.

Topic-Specific Diagnostics for LDA and CTM Topic Models

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

Small tutorial on how you can use BERT for Topic Modeling

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

Analysis of social media conversations surrounding COVID-19 in Singapore

Code to train a LSI model using Pubmed OA medical documents and to use pre-trained Pubmed models on your own corpus for document similarity.

Ignis: Iterative Topic Modelling Platform

Topic Modelling to segregate news report data to different topics using Gensim, NLTK, Spacy.

I don't feel like reading this research paper that my professor has us doing so I made this summarizer using Latent Dirichlet Allocation

Generic fake news detector across news domains, using feature engineering. Fully implemented POS tagging, sentiment analysis, emotion detection, topic modelling and named entity recognition.

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

In this module you will learn how to analyze topic modeling output from Amazon Comprehend, then perform topic modeling on two documents with a known topic structure.

Brand Slogans - Analysis of Slogans, Logos and Gender of CEOs in Brands and Companies​

Identification of papers/websites/documents with context related to a given context or abstract | Runners-up at Megathon 2k19 for Qualcomm

Scrape Reviews about Amazon Shopping App on Google CH Play and Perform Topic Modelling by LDA (Latent Dirichlet Allocation) using Python

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.

This chapter covers the Amazon Rekognition service for analyzing the content of the images using various techniques. You will learn how to analyze faces and recognize celebrities in images. You will also be able to compare faces in different images to see how closely they match with each other.

Predict which Ted-Talks belongs to which topics using Topic Modelling

A Data Analysis Project on the Tunisian and Egyptian Springs

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

Analysing the top news headlines topics in Australia.

Investigating biases in scientific publications

A Blog application with features such as generating relevant tags and keywords from blogs in order to generate recommendations for users. Application made using Django. Joint Project by Swebert Correa and Jinit Sanghvi.

Materials and approach utilized for competing in 10-day long hackathon hosted by Analytics Vidhya. Given a set of research articles, it's category has to be classified.

A Pytorch Implementation of LDA2VEC : "Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec"

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 is an end-to-end NLP project based on text classification. We have created a real-time web application that takes input text from the user and predicts its diagnosis out of 10 predefined labels.