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Social media has extensive content volunteered by drug users, who also mentions drug side-effects. Such mentions may indicate side-effects that are heretofore unknown to the medical professionals and researchers, pharmaceutical companies, and regulators. To study and monitor the drug and its potential symptoms and side-effects for epidemiological surveillance of drug trends, the research needed a higher level of knowledge extraction and improvement in domain knowledge. Our aim is to methodically classify the tweets that mention drug side-effects, and by applying our model we improved the medical knowledge extraction from our data-set. We crawled 7 million tweets related to marijuana concentrate and cannabis (known as marijuana) the tweet text mentioned about marijuana usage, marijuana consumption, the medicinal value of marijuana and symptoms or side-effects of marijuana. It is essential to identify the drug side-effects in the tweets in order to create a bag of words relating to each category of marijuana. This will assist us in the classification of tweets under different categories of marijuana-based on side-effects. We manually created multi-domain knowledge, modeled in DsOn(Drug and Symptom Otology), to facilitate the extraction of semantic parsing from people opinion on Social Media, through a combination of medical taxonomy dictionary, and semantics relationship based techniques from UMLS, PubMed, and DBpedia. Experiment results showed an improvement in recall from 0.14 (without ontology model) to 0.23 (with ontology model).