Sentiment Analysis source code: the code performs sentiment analysis on a user's speech input using the SpeechRecognition library and NLTK (Natural Language Toolkit) library in Python.

The sentiment_analysis() function tokenizes the input text into words, removes stop words(common words like "the" and "and" that don't add much meaning), and then computes the sentiment scores using the VADER (Valence Aware Dictionary and sEntiment Reasoner) lexicon, which is included in NLTK. Based on the sentiment score, it returns a label of either positive, negative, or neutral.

The analyze_voice_sentiment() function: uses the Recognizer class from the SpeechRecognition library to listen to the user's speech input through the microphone and recognize it using the Google Speech Recognition API. Then, it calls the sentiment_analysis() function to perform sentiment analysis on the recognized text and returns the sentiment label.

Finally, the sentiment_label variable stores the sentiment label returned by the analyze_voice_sentiment() function, which is printed to the console.