multimodal social media content (text, image) classification

Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake

Kaggle Twitter US Airline Sentiment, Implementation of a Tweet Text Sentiment Analysis Model, using custom trained Word Embeddings and LSTM-Deep learning [TUM-Data Analysis&ML summer 2021] @adrianbruenger @stefanrmmr

System developed by team datamafia in WNUT 2020 Task 2: Identification of informative COVID-19 English Tweets

Tweet Text Writer Recognition Application

ML model to extract the main concept from a tweet trained on a dataset built with Babelnet and Babelfy.

Build a classifier that can extract a useful tweet among a ton of tweets during COVID-19 epidemic.

Simple Repository regarding tweet classification using huggingface tokenizer and transformer, and tracking using weights and biases.

Treatment evaluation in presence of large number of covariates or treatment heterogeneity through Machine Learning methods

This is a self-created basic tutorial for the Machine Learning II course at the Language Analysis and Processing master's degree where I show how I retrieve tweets from Twitter App and visualize, cluster and classify data by means of ML techniques and algorithms.

NLP Course By Deep learning.io powered by @coursera. Taught by: Younes Bensouda Mourri, Instructor of AI at Stanford University and Łukasz Kaiser, Staff Research Scientist at Google Brain.

EDA and Modeling Attempt for multi class text classification.

Welcome to our project, where we leverage advanced sentiment analysis techniques to detect and classify toxic content in game-related tweets. Our goal is to develop a predictive model that can accurately identify toxicity based on the language used in these tweets.

Exploring Jaccard-similarities technique on tweets then visualizing its output using k means and k means with pca. Additional input on time series analysis, web scrapping and twitter Scrapping.

Using Pandas, Pickle, ReGex, Tweepy, Scikit-Learn, Sastrawi. NLTK, and bs4

We aim to clasify tweets based on three categories 0: hate, 1: offensive, 2: neutral. For this purpose we use HateBert pretrained model with RNNs as the trainable layers

Classify whether a tweet constitutes a rumour event

Predict which Tweets are about real disasters and which ones are not

Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!

This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.