Today, we are all living in a digital age where data is entangled in our daily lives. However, since most of this data is unstructured and the volume of it is large, it requires statistical libraries and machine learning (ML) to apply it to technology solutions. The NLTK libraries serve as a framework for us to work with unstructured data, and sentiment analysis serves as a practical use case in NLP. Sentiment analysis, or opinion mining, is a type of supervised ML that requires a training dataset to accurately predict an input sentence, phrase, headline, or even tweet is positive, negative, or neutral. Once the model has been trained, you can pass unstructured data into it, like a function, and it will return a value between negative one and positive one. The number will output decimals, and the closer it is to an integer, the more confident the model's accuracy will be. Sentiment analysis is an evolving science, so our focus will be on...