Alteryx Foundation Practice Exam 2026 – Complete Prep Guide

Sentiment analysis is specifically designed to identify and categorize the emotions expressed in text, distinguishing between positive and negative sentiments. This technique leverages natural language processing (NLP) to analyze the language used in the text, allowing it to recognize the tone and sentiment of the words, phrases, and sentences. This process is crucial for applications such as social media monitoring, customer feedback analysis, and market research, where understanding public sentiment can inform business decisions.

Other analysis types, while important in data analysis, do not focus on sentiment. Descriptive analysis summarizes data features, inferential analysis makes predictions or inferences about a population based on a sample, and comparative analysis involves comparing two or more datasets to find differences or similarities. Each of these methods serves different purposes that do not include the evaluation of sentiment in text data.