Submission for CL4HEALTH @ LREC-COLING 2024
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Updated
May 28, 2024 - Jupyter Notebook
Submission for CL4HEALTH @ LREC-COLING 2024
Classification Report Sentiment Analysis and Trend Analysis
This repo contains Big Data Project, its about "Real Time Twitter Sentiment Analysis via Kafka, Spark Streaming, MongoDB and Django Dashboard".
Image recognition model made to recognize financial charts from social media sources.
Analysis of censored tweets. Undestanding the topics that are censored in different countries using different NLP techniques
Classification of Covid-19 Tweets using Multinomial Naive Bayes and TF-IDF Vectorizer to categorize tweets about Covid-19 into three main classes
Utilizing Natural Language Processing (NLP) to analyze and classify tweets for detecting disaster-related content.
This repository presents my bachelor project titled "Mapping and Tracking Sentiment Arcs in Social Media Streams"
This projects contains a nlp pipeline for topic labelling with BERTopic
Web application for financial data analysis, based on a machine learning classification model for news and social media posts, enabling price predictions
Streamlit Dashboard to analyze the sentiments of Tweets about US Airlines
We compare the performance of multiple BERT-based models for the task of Emotion recognition in Arabic Tweets.
Frontend project for the Classification Tweets project
Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).
This repository contains code needed to replicate the master thesis study, "Refugees Welcome? A comparative sentiment analysis of tweets in Germany surrounding inflows of Syrians and Ukrainians", by Andrea Cass
This machine learning project specifically focuses on detecting spam tweets, and provides a step-by-step guide to building a classifier using three kinds of classifiers: Decision Tree, Naive Bayes, and Logistic Regression.
A Transformers-based model that classifies the political bias of Brazilian Portuguese tweets as either conservative, liberal, or neutral.
In this project, we're going to create a recommend neural network and create it on a tweet emotion data set to learn to recognize emotions in tweets.
Notebook used to explore and classify 500,000 tweets about Elon Musk in an unsupervised manner.
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