The article by David Kyle provides a detailed guide on deploying a sentiment analysis model using natural language processing (NLP) to evaluate the sentiment of comments as either positive or negative. The process involves using a pre-trained sentiment analysis model from Hugging Face, deployed to Elasticsearch, to analyze customer reviews from the 2015 Yelp Dataset Challenge. The deployment process is facilitated by the Eland docker agent and involves setting up an ingest pipeline in Kibana to classify reviews. The article illustrates the procedure with examples, showing how comments are labeled with predicted sentiment values and their associated probabilities. It highlights the utility of sentiment analysis in understanding customer feedback, with a practical example revealing that approximately 44% of the analyzed Yelp reviews are positive, although the model mislabels a small fraction. The guide encourages experimentation with NLP features in Elastic Stack, promoting a 14-day free trial to explore further applications, such as text embeddings and named entity recognition.