Random Logistic Vector Analysis Based Opinion Mining For Identifying Best Product Using User Reviews in Ecommerce Applications

Abstract

Social Media (SM) has emerged as a new communication channel between consumers and enterprises to generate a large volume of unstructured text data about products. Many web users post their opinions on several products through the blog, review sites and social networking sites-based text of the attitude. Customer feedback plays a very important role in the daily movements of products. Opinions of others are also taken into account when making decisions to select the best products. Event though, it reads reviews of all the customers, it has difficulty in making decisions based on the information about whether or not to purchase the product. Keeping track of the customer's opinion, manufacturers are also finding it difficult to manage the products which lead to economic collapse. To address this problem, the proposed Random Logistic Vector (RLV) algorithm is used to analyze the product quality and life of the products based on reviews. The first process is data collection based on customer content-based reviews about products from Ecommerce applications. Then, collected data are trained into preprocessing to remove unwanted data and noise. Secondly, preprocessed data are trained into feature extraction to select the best features of the lexicon-based sentiment words, adverbs, adjectives word based on consumer reviews about products from the dataset. Finally, feature extraction data are trained into the proposed Random Logistic Vector (RLV) algorithm is done to identify the polarity or subjectivity orientation that indicates the customer opinion text expressed by the user or client in terms of value. Random Logistic Vector (RLV) algorithm which is used to classify the data to help select the best products and analyze the product quality. It will also lead to the economic growth of productive enterprises.