This research proposes a new framework for analyzing Arabic opinions and measuring the sentiment analysis for Arabic contents using semantic approach and text analysis. The proposed framework detects the Arabic opinion orientation using Arabic ontology and Part Of Speech Tag ‘POS’ for Arabic words. The framework consisted of five components. These are; the Arabic Part Of Speech “POS” tagger which was used to assign the correct tag for every word in the opinion, the Arabic Ontology Classifier to query RDF Ontology using ontology engineering; the SPARQL language to extract the main concepts, the third component is Arabic Sentiment Lexicon which was used as the Arabic dictionary; and the final components are the Counter and Report components which perform the calculation and display the final results and reports. A framework was designed using advance integration technologies such as Stanford POS tagger, Stanford Ontology protégé, and Jean framework from Apache which was used to integrate the Ontology. The framework was tested against Arabic comments using the Arabic movie ontology, and the results showed that the framework was successfully able to detect and classify the pilot comments written in Arabic language and measure them against the five star rating system. The results also agreed well with the calculated stars which was entered by the opinion holders.