13. A system for constructing a domain-specific sentiment classifier for classifying sentiment expressed by documents in a specified domain, comprising: a computer-readable storage medium storing executable modules comprising: a scoring module configured to score sentiments expressed by a plurality of domain-specific documents responsive to a domain-independent sentiment lexicon; an analysis module configured to identify a set of high-sentiment documents responsive to the scoring; a lexicon module configured to create a domain-specific sentiment lexicon based at least in part on the set of high-sentiment documents; a classifier module configured to generate the domain-specific sentiment classifier responsive to the domain-specific sentiment lexicon; and a storing module configured to store the domain-specific sentiment classifier; and a processor configured to execute the modules stored by the computer-readable storage medium.
13. A system for constructing a domain-specific sentiment classifier for classifying sentiment expressed by documents in a specified domain, comprising: a computer-readable storage medium storing executable modules comprising: a scoring module configured to score sentiments expressed by a plurality of domain-specific documents responsive to a domain-independent sentiment lexicon; an analysis module configured to identify a set of high-sentiment documents responsive to the scoring; a lexicon module configured to create a domain-specific sentiment lexicon based at least in part on the set of high-sentiment documents; a classifier module configured to generate the domain-specific sentiment classifier responsive to the domain-specific sentiment lexicon; and a storing module configured to store the domain-specific sentiment classifier; and a processor configured to execute the modules stored by the computer-readable storage medium. 19. The system of claim 13 , wherein the classifier module is further configured to: build a model having n-grams of the domain-specific sentiment lexicon as features; and train the model on a training corpus having domain-specific documents having manually-labeled sentiment scores.