metadata

title: Tweet NLP Sentiment Analysis
emoji: 🐦
colorFrom: indigo
colorTo: gray
sdk: streamlit
app_file: app.py
pinned: true

Configuration

title: string
Display title for the Space

emoji: string
Space emoji (emoji-only character allowed)

colorFrom: string
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)

colorTo: string
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)

sdk: string
Can be either gradio or streamlit

sdk_version : string
Only applicable for streamlit SDK.
See doc for more info on supported versions.

app_file: string
Path to your main application file (which contains either gradio or streamlit Python code).
Path is relative to the root of the repository.

pinned: boolean
Whether the Space stays on top of your list.

HISTORY OF THIS PROJECT

This project began when I saw the Twitter announcement by Facebook that they were re-branding to Meta, and I was curious what the public sentiment of this announcement would be. I suspected that it would be more negative than positive due to various reasons, but I wanted to find out nonetheless.
Tweet replies from the Facebook/Meta announcement were extracted using Twitter's API, then saved to a CSV file.
Due to Twitter's developer policies, I cannot share that file of extracted Tweet replies and associated metadata, so I ran sentiment analysis on those Tweet replies outside of this app and saved the results to the df_redacted.csv file. This file DOES contain the ids of the tweets which were analyzed, which is allowed per Twitter's policies. The sentiment model I used was VADER.
In future revisions, I plan on finding a way to post my code from the extraction portion of the project, along with demonstrating various methods of cleaning the Tweets and how that affects the outcome of the analysis.