This Python script leverages Selenium and NLTK to analyze the sentiment of recent chat messages on a Twitch stream.

1. Retrieves Chat Messages:

   • Uses Selenium to automate the process of loading the specified Twitch channel's chat.
   • Extracts the latest chat messages from the chat log.

2. Calculates Sentiment Scores:

   • Employs NLTK's SentimentIntensityAnalyzer to assign a sentiment score (positive, negative, neutral) to each chat message.

3. Determines Average Sentiment:

   • Calculates the overall average sentiment of the collected chat messages.

• Prerequisites:
  • Python 3 (https://www.python.org/)
  • Selenium ( pip install selenium)
  • NLTK ( pip install nltk)
  • A WebDriver for your browser (e.g., ChromeDriver for Chrome: https://chromedriver.chromium.org/)

1. Customize the Code:

   • Replace /usr/bin/chromedriver with the correct path to your WebDriver.
   • Change "https://twitch.tv/summit1g" to the desired Twitch channel URL.

2. Run the Script:

   python main.py
   

Using React to Update a Python Lexicon Database

Conceptual Overview

1. React Frontend: Design a user interface for lexicon management.

   • Input fields to add new words or phrases to the lexicon.
   • Editing areas to modify existing entries.
   • A display component to show the current lexicon contents.
   • Buttons to trigger actions (add, edit, delete)

2. Python Backend (API): Create a REST API using frameworks like Flask or Django to handle database interactions.

   • Endpoints:
     • GET /lexicon : Fetches the entire lexicon.
     • POST /lexicon: Adds a new entry.
     • PUT /lexicon/<id>: Updates an existing entry (where <id> is the entry's identifier).
     • DELETE /lexicon/<id>: Deletes an entry.

3. Database: Choose a database suitable for storing your lexicon.

   • SQLite