While traditional information extraction systems have been built to answer questions about facts, subjective information extraction systems will answer questions about feelings and opinions. A crucial step towards this goal is identifying the words and phrases that express opinions in text. The computational treatment of opinion, sentiment, and subjectivity has recently attracted a great deal of attention (see references), in part because of its potential applications. For instance, information extraction and question-answering systems could flag statements and queries regarding opinions rather than facts. The automatic extraction of opinions, emotions, and sentiments in text to support applications such as product review mining, summarization, question answering, and information extraction is an active area of research in NLP. Many approaches to opinion, sentiment, and subjectivity analysis rely on lexicons of words that may be used to express subjectivity. Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. This system proposes the concept of polarity consistency of words/senses in sentiment dictionaries.