You have built an Urban Dictionary Emoji/Slang Sentiment Analyzer with Python and Twilio SMS that analyzes the sentiment of a text message received from a user's phone number, searches for the query in Urban Dictionary's API, and returns the results to the user. The application uses Flask as the web framework, NLTK for natural language processing, and Twilio's Python Helper Library for interacting with Twilio APIs. It authenticates its Twilio account credentials using environmental variables stored in a .env file. After setting up the environment, you can run the application by opening a ngrok tunnel to port 5000 and configuring the Twilio phone number to forward incoming messages to the application's webhook. The application analyzes the sentiment of the text message, finds the top 10 most frequent words, and returns the results to the user.