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Abstract:The world of technology is growing by leaps and bounds and the arena in technology that is going to be explored is Data Mining. It is estimated that till 2025, most of the world's trade will be based on Data Mining [1]. There is vast availability of people opinion data on twitter for almost every product and service. The challenge is to interpret this data and to extract the information which can lead a decision maker to take better decisions. In dictionary-based approach every word with some positive, negative or neutral value is mapped but opinions are not always direct, hence the sense of the sentence or sub-sentence doesn't agree with its numeral weight. This short coming of this approach lead us to come up with some strategies to increase the accuracy of this method by multiplying the weights together and using some fundamental semantic rule to classify sarcastic tweets. Hence in this paper a hybrid approach is implemented which ensures the sign of total weight of the sentence according to its indirect sense. The positive outcome is that opinions which were earlier treated as neutral are now retaining their sense and add up to our decisions. The hybrid approach is using the concepts of dictionary-based approach and semantic-based approach i.e. matching words from the dictionary and assigning their sentimental value and also using some specific semantic rules used for analyzing sarcastic or neutral tweets for gaining more information about the opinions. The proposed mining of opinions has become easier and more accurate that can be utilized for product's sale forecasting.