Opinion Mining (OM) is a field of study in Computer Science that deals with development of software applications related to text classifications and summarizations. Researchers working in this field contribute lexical resources, computing methodologies, text classification approaches, and summarization modules to perform OM tasks across various domains and different languages. Lexical and computational components developed for an Opinion Mining System that processes Hindi text taken from weblogs are presented in the paper for the demonstration. Text chosen for processing are the ones demonstrating cause and effect relationship between related entities ‘Food’ and ‘Health Issues’. The work is novel and lexical resources developed are useful in current research and may be of importance for future research in the field. The resources are developed for an algorithm ‘A’ such that for a sentence ‘Y’ which is a domain specific sentence from weblogs in Hindi, A(Y) returns a set F, HI, p, s such that F is a subset of set, FOOD=set of word or phrases in Hindi used for an edible item and HI is a subset of set, HEALTH_ISSUE= set of word or phrases in Hindi used for a part of body composition ‘BODY_COMPONENT’ UNION set of word or phrases in Hindi used for a health problem a human being face ‘HEALTH_PROBLEM’. Element ‘p’ takes numeric value ‘1’ or ‘-1’ where value ‘1’ means that from the text ‘Y’, algorithm ‘A’ computationally derived that the food entities mentioned in set ‘F’ have a positive effect in health issues mentioned in set ‘HI’ and the numeric value ‘-1’ means that the food entities in set ‘F’ have a negative effect in health issues in set ‘HI’. The element‘s’ may take value ‘1’ or ‘2’ indicating that the strength of polarity ‘p’ is medium or strong.