This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.
['Vincent Ng', 'Sajib Dasgupta', 'S. M. Niaz Arifin']
Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews