A PERCEPTION CLASSIFICATION USING HYBRID FEATURE EXTRACTION

The estimation of product quality is more signicant during the purchasing of online products. Therefore, many opinion mining and sentiment classication methods were introduced to purchase the best products through online shopping. But, these classication methods haven't attained the effective product classication with best reviews and ratings. Several popular classication algorithms estimated along with three ltering Methods. Presented a hybrid technique to extract the product features. In this method, association rules and point-wise mutual information is combine. Large amount of features of products, not only to increase the time of computation but also to increase accuracy of classication. Optimize the amount of marketing funds spent on each customer, and just making a binary decision on whether to market to him. General Purpose Emotion Lexicons (GPELs) that associate with emotion categories remain a valuable resource for emotion analysis. Proposed a hybrid feature extraction method PCA (Principle Component Analysis) using lexicon-based method to classify and separate the products from the large set of different products depending on their features, best product ratings and positive reviews.