Shopping Assistant: A Fashion Suggesting Intelligent System using Natural Language Processing: An Aspect Based Opinion Mining Approach

Abstract

In the 21st century, the usage of Web 2.0 shows a vast increase in its growth with highly attracting more users each year by allowing them to carry out most of the activities online. After the impact of Covid19, most of the fashion cloth and textiles industries transferred from the physical to the digital world. When it comes specifically to clothing and accessories shopping, people always look for new styles, fashions, and brands in the market. However, it is challenging to find high-quality products which meet the exact needs in an online scenario. The customer reviews and ratings play a vital role in helping customers find quality items that best match the need. With the advancement of social media, opinionated information and reviews available on the web are vast. Therefore, it is hard to manually go through and compare every review, and it is a time and energy-consuming task. The lack of personalized suggestions given to the users depending on customer opinions, user preferences, and their factors makes them difficult to choose from various items. Getting to know about own customers and their behaviours is very important for a success of a business. Only having the customer details and their purchased details would not yield a clear insight. Without knowing the targeted groups of customers and the product’s strengths and weaknesses, the retailers cannot improve their sales. In addition, there is no option for the users to try on clothes and accessories in online scenarios before they make the purchase. The customers must take the body measurements manually and decide on a size that would fit their body. This paper presents a ‘Fashion Suggesting Intelligent System’ which addresses the current problems the customers and retailers facing in the online fashion retailing industry using Opinion Mining, Aspect-Based Sentiment Analysis, Personalized Suggestions and Augmented reality. The research proposes a hybrid approach that combines unsupervised and supervised learning to enhance sentiment analysis. The results from the sentiment analysis are used in providing personalized suggestions to customers and intelligent insight to merchants. The proposed system aims to assist customers and retailers while shopping and selling online by providing an intelligent system that can analyze customer’s opinions at an aspect level and provide personalized suggestions, deep customer insight and targeted groups to of customers. In addition, the system combines augmented reality in a traditional online shopping context to virtually fit on clothes before making a purchase.