Sentiment analysis in one of the earliest NLP tasks that has had direct real-world impact in a number of industries. It now has a widespread and practical application in review mining, customer management, social media analysis, news analysis, healthcare support and decision support in the finance industry. Pang, Lee and Vaithyanathan (2002)'s paper is a pioneering work that has enabled NLP to make this impact. It is amongst the first works in sentiment analysis and helped define the subfield of sentiment and opinion analysis and review mining. It has become the go to paper for anyone starting work in this area. This work has had research, application and data impact. The paper introduced a new way to look at document classification, developed the first solutions to it using several machine learning methods and feature combinations, and presented insights into and challenges of sentiment classification. Beyond the task formulation and technical methods, this paper also had significant data impact. The movie review dataset has supported much of the early work in this area and it still is one of the commonly used benchmark evaluation datasets. There are two key reasons for this success: (a) emphasis on making the data widely available; and (b) carefully curating the data, for example to avoid domination of prolific reviewers. The data is extensively used in courses, and is part of NLTK as a core application to start for students interested in NLP. The insights and challenges discussed in this work have provided the basis of many works and is still driving new research today. According to recent statistics it is the highest cited EMNLP paper. With over 6800 Google scholar citations, and over 400 Google scholar citations in 2017 alone, this work has stood the test of time. Given the award time constraints, it is the last opportunity for this paper to be considered.