The Bag Of Words' performance is limited because to inherent flaws in its treatment of the polarity shift problem; the bag of words representation considers the two opposite texts to be quite similar. To address this issue, a model known as dual sentiment analysis (DSA) has been developed. Dual Sentiment Analysis expands sentiment categorization analysis from one side of a review to two sides of a single review. For both training and test reviews, a data expansion strategy is presented to solve sentiment reversed review. In the sentiment classifier, a dual training method is utilized to train the original and reversed reviews. The Dual Sentiment Analysis paradigm divides polarity categorization into three categories (positive, negative, and neutral) (positive, negative and neutral). A corpus-based method for constructing a pseudo-antonym dictionary is devised to remove dual sentiment analysis' reliance on an external antonym dictionary for review reversal. The findings show that DSA is effective in supervised sentiment analysis.