which was created on a request from an overseas firm, processes chats, blogs, and forums to provide
as output generalized negative and positive scores as well as specific subjectivity terms associated
with the product’s name given in user’s query. The system is based on the highly structured
ontology and linear grammar. The ontology consists of syntactic and semantic terms ad their subcategories,
groups and classes. Semantic terms are those ones expressing subjectivity and having
either positive or negative semantic orientation. Syntactic terms do not express subjectivity but
can modify the intensity of polarity of semantic terms. The relation between semantic and syntactic
terms is defined as a binary relation and groups with symmetric, asymmetric, and inverse relations
between syntactic and semantic terms are distinguished. Semantic terms are assigned scores from
1 (–1) to 9 (–9). The paper suggests differentiating between formal and linguistic ontologies. Linear
grammar comprises rules that allow mapping ontology terms onto the names of products and generate
phrases with semantic and syntactic terms. The overall system’s efficiency measured in terms
of recall and precision of information retrieval achieves 86%.
automatic customers’ opinion mining, ontology, linear grammar, quality evaluation, commercial product.