Topic modelling and text analysis of OECD "Studies on Water" corpus.
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Updated
Oct 10, 2024 - Jupyter Notebook
Topic modelling and text analysis of OECD "Studies on Water" corpus.
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Text mining: Using LDA to create a topic model to cluster news articles
Contains .pptx slides I used for my NLP class. Includes an introductory slide which deals with text pre-processing, and then proceeds with text classification, sentiment analysis, named entity recognition, topic modelling, finding similarity between documents, text generation and finally, language translation. Worked out ANN, CNN and RNN too.
Within this repository you are going to find all the material needed to attend this two-classes course once it will be ready. The notebook (for which we suggest you to run via Google Colab) and slides for the class can be downloaded from this repository.
Interface for easier topic modelling.
A set of methods for finding an appropriate number of topics in a text collection
NLP: topic modelling using BERTopic + LSA + pLSA + LDA
A sentiment analysis project using Twitter tweet data aimed at analysing the sentiment towards Ukrainian and Syrian refugees.
This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv
Analysis of Google PlayStore reviews for “League of Legends: Wild Rift” and “Mobile Legends: Bang Bang”
My published paper on the application of LDA on documents. Base corpus: Thousands of LDS General Conference articles spanning decades.
This repository showcases an interactive conversational analysis of the Cornell Movie-Dialogs Corpus, visualised using D3.js, as part of the Data Analysis & Visualisation (DS3001) course final project, encompassing 220,579 exchanges between 10,292 character pairs in 617 movies.
Top2Vec learns jointly embedded topic, document and word vectors.
Analysis of censored tweets. Undestanding the topics that are censored in different countries using different NLP techniques
Topic Modelling & Sentiment Analysis of Data Science Subreddit
Topic modelling dengan LDA dan Sentiment Analysis pengguna Twitter dan Facebook terhadap berita TikTok Shop Dihapus
Topic modelling data collection and analysis with Python for LLaMA dataset
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