Identification of causal pattern using opinion analysis in Indonesian medical texts

Medical text extraction has become needs for researcher and society to generate factual knowledge about disease diagnosis. Disease diagnosis information is usually related to causality, symptom, and disease. Today, source of information about causality, symptom, and disease are able to be obtained from print and electronic media. The research focused on experiment and analytic related to identification of health information which is related with causality. Causality is a sentence explains about cause and effect. Some of extraction patterns to identify causality sentence have been done by researcher to generate discourse marker. However, discourse marker cannot be found in every sentence which obtains cause and effect meaning. Therefore, this research suggests a new approach to identify a correlation of causality sentences using analytical opinion pattern. Positive sentences (+) can represent an explanation of cause sentence, whereas negative sentences (-) can represent an explanation of sentence effect. Classification method that is used to identify opinion sentences is Lexicon, Naïve Bayes and SVM with 230 amount of training. The experiment using 3 chronological scenarios generate an average result as follows: Lexicon propose generates 50,14; 50,87; 49,71 while Naïve Bayes method generates 42,32; 43,33; 45,07 and SVM method generates 97,68; 97,97; 95,19. Recall measurement generates 50% from 10 sentences random.