Big data analytics has a significant impact on real-time purchasing and e-commerce, and it's being used to boost sales and enhance consumer interactions. Customers increasingly rely on online marketing to find the best deals on high-quality goods. Social media activity on both sides of the transaction might reveal information about the customer's purchase experience and opinions about the business. Using Sentimental Analysis (SA), one can figure out how something makes you feel. SA examines people's ideas and feelings about a product. The importance of acquiring insight into customers' sentiments when purchasing things is reduced when SA is weak. Also, the influence of understanding the consumers' impression of a product is reduced. For sentiment analysis in big data, this research proposes a Perceptive Genetic Algorithm-based Wolf Inspired Classifier (PGAWIC). For sentiment analysis in large product review datasets, PGAWIC draws inspiration from wolf foraging behavior. Better optimization for classifying the sentiments is achieved using genetic algorithms. The proposed classifier is evaluated using MATLAB with the metrics namely precision accuracy and f-measure performance measures on a four-product review dataset. According to the results, the proposed classifier PGAWIC is more accurate than the current classifiers in classifying the sentiments.