Product Aspect extraction in opinion mining: a Survey - Khan, Zafar Ali; Mahalakshmi, R (International Journal of Computer Sciences and Engineering, 2025)
Today’s E-commerce development growths are very high in all fields such as online purchase, education, medical etc., the trend BtoC Business to Customer has been changed to CtoB Customer to Business, many customers like and preferred to buy the products on online shopping, due to time constrain, traffic, tracking system, discount, and also one advantages which is available in online not in traditional shopping, it’s very easy to compare with other products. Reviews play a vital role to customers and merchants, using the reviews merchants are trying to give the best quality product, best price and discount to the customers, so they can improve the profit and increase the number of customers. Customers while purchasing the product, it is very difficult and impossible to read all the reviews. There are many algorithms available to recommend and rank the product to the customer, but if the input given to the system is incorrect then the output will not be in accurate manner as per the user request, this survey overview the different aspect extraction techniques and approaches. And also identified the research gaps and propose a recommendation system for online purchase using customer reviews, the proposed system has four phases i. Pre-processing ii. Aspect identification (explicit and implicit) iii. Semantic classification and Aspect polarity identification iv. Efficient Product Aspect Based Ranking.