Blazing fast topic modelling for short texts.
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Updated
Jul 1, 2024 - Python
Blazing fast topic modelling for short texts.
learning python day 15
FinABSA is a T5-Large model trained for Aspect-Based Sentiment Analysis specifically for financial domains.
Python package to clean raw tweets for ML applications.
An off-the-shelf pre-trained Tweet NLP Toolkit (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Tweebank-NER dataset
Like and retweet your tweets, or search tweets by topic. It stores and serves data with a Flask webapp. 🐦 Live demo running on twitter.com/ai_testing
Hashformers is a framework for hashtag segmentation with Transformers and Large Language Models (LLMs).
Assignment to scrape tweets based on hashtags and then analyse them through a sociological lens.
Scrape tweets without authentication using Selenium WebDriver
Implementation of an ETL process for real-time sentiment analysis of tweets with Docker, Apache Kafka, Spark Streaming, MongoDB and Delta Lake
Easily search tweets for topic-relevant hashtags.
VADER-analysis of recent tweets by keyword
Generate word clouds based on Twitter content
twig.py - a twitter web3 influencer truffle pig used for finding engaged users
Sentiment Analysis of Live Tweets on Covid-19 pandemic
Analyzing Disaster Tweets using natural language processes
En esta práctica se empaqueta y distribuye una aplicación Python que descarga y analiza tweets en función de puntuaciones de sentimiento. Los resultados del análisis se guardan en una base de datos MongoDB, y la información se muestra en la web.
A sentiment analysis tool for tweets
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