We present Sentiment Analyzer (SA) that extracts sentiment
(or opinion) about a subject from online text documents.
Instead of classifying the sentiment of an entire document
about a subject, SA detects all references to the given
subject, and determines sentiment in each of the references
using natural language processing (NLP) techniques. Our
sentiment analysis consists of 1) a topic specific feature
term extraction, 2) sentiment extraction, and 3) (subject,
sentiment) association by relationship analysis. SA utilizes
two linguistic resources for the analysis: the sentiment lexicon
and the sentiment pattern database. The performance
of the algorithms was verified on online product review articles
(“digital camera” and “music” reviews), and more
general documents including general webpages and news
articles.