  Sentiment analysis refers to the use of natural language processing to
identify and extract subjective information from textual resources. One
approach for sentiment extraction is using a sentiment lexicon. A sentiment
lexicon is a set of words associated with the sentiment orientation that they
express. In this paper, we describe the process of generating a general purpose
sentiment lexicon for Persian. A new graph-based method is introduced for seed
selection and expansion based on an ontology. Sentiment lexicon generation is
then mapped to a document classification problem. We used the K-nearest
neighbors and nearest centroid methods for classification. These classifiers
have been evaluated based on a set of hand labeled synsets. The final sentiment
lexicon has been generated by the best classifier. The results show an
acceptable performance in terms of accuracy and F-measure in the generated
sentiment lexicon.
