LexiPers: An ontology based sentiment lexicon for Persian
This work addresses the need for sentiment analysis resources in Persian, which is an incremental contribution to domain-specific NLP tools.
The authors tackled the problem of generating a general-purpose sentiment lexicon for Persian by introducing a new graph-based method for seed selection and expansion based on an ontology, mapping it to a document classification problem using K-nearest neighbors and nearest centroid methods, and achieving acceptable performance in terms of accuracy and F-measure.
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.