Sentiment analysis has enormous contributions and vivid utility in many areas of natural language processing, e-commerce and social network analysis. Although there are several methods designated for this task but major thrust of this domain to develop machine learning based polarity classification of user generated text, review and post. It is even tougher to classify the text containing multi-polarity words. In the meantime, several studies have discussed and focused on contextual information of word in Natural Language Processing. But, due to lack of methodologies, it becomes challenging for contextual polarity disambiguation because no evidence is available which proves that whether the sentiment expressed by the context is correctly identified. Therefore, in this work an effort to compute the quantified amount of polarity of terms/words in a given text, based on its local as well as global contexts. The proposed work contains two major parts, firstly it extracts the local and global features from semantic dependency parsing and co-occurrences with which association of the designated word to be disambiguated with previous polarities. Then, the second part computes a Bayesian belief network of these features along with their conditional probabilities which are used to disambiguate and compute sentiments. The performance of the proposed scheme is exhaustively tested on standard datasets and results clearly show the efficacy of the proposed scheme.