Python Notebooks for Collecting Tweets and Analyze their text using various text classification and clustering techniques
-
Updated
Apr 3, 2019 - Jupyter Notebook
Python Notebooks for Collecting Tweets and Analyze their text using various text classification and clustering techniques
A twitter sentiment analysis project in a jupyter notebook.
Collection of notebooks written while learning to work with the Twitter API
Wrangling and analysis of Tweets from WeRateDogs (@dogrates) with Python in Jupyter Notebook. Project focuses on gathering, assessing and cleaning data. Various methods, including Python's Requests and Tweepy packages for performing a GET Request and querying Twitter API, were used to collect Tweets and relevant data available online.
Sentiment Analysis application for the presidential elections Ecuador 2021 using Twitter API. Also a text classification model with SVM, Naive Bayes and Random Fores made in python notebook. University of Cuenca Text Mining course 🎓
This repository explains the code for collecting the data from Twitter API. The code is saved in Jupiter notebooks, follow the guideline in the README file.
Created a Python script to perform a sentiment analysis of the Twitter activity of various news outlets. These findings are visualized in both a scatter plot and a bar chart. Skills Needed: Python, Pandas Library, Jupyter Notebook, Tweepy, TextBlob Matplotlib and Seaborn
Add a description, image, and links to the tweepy topic page so that developers can more easily learn about it.
To associate your repository with the tweepy topic, visit your repo's landing page and select "manage topics."