This document provides an introduction to sentiment analysis. It begins with an overview of sentiment analysis and what it aims to do, which is to automatically extract subjective content like opinions from digital text and classify the sentiment as positive or negative. It then discusses the components of sentiment analysis like subjectivity and sources of subjective text. Different approaches to sentiment analysis are presented like lexicon-based, supervised learning, and unsupervised learning. Challenges in sentiment analysis are also outlined, such as dealing with language, domain, spam, and identifying reliable content. The document concludes with references for further reading.