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Aspect Based Intelligent System for Measuring Customer Loyalty

EasyChair Preprint no. 8592

7 pagesDate: August 3, 2022


Today’s generation like to purchase online things. Online market is growing very fast. As more retailers for this market appears, the battle to sell things becomes fiercer. Consumers are much more intelligent; while making a buy, they investigate and evaluate options. Some consumers are still hesitant to make purchases online, while some of them are regular buyers. People becomes very conscious of the necessity of buying online as a result of numerous disadvantages, A system is much needed which provide a proper analysis of regular buyer to facilitate new customer and company. In this research a wise technique is offers to measure customers loyalty to a product it helps news customer to take decision faster and also assist new customers. It is necessary to have a fast and efficient technique for clients. Our technique employs a unique concept for determining a devotion of a buyer to a particular brand or item, and it may be of assistance to a new customer in making a choice made regarding a certain item based on its many functions and past customer comments. In our proposed model we used artificial neural network (ANN) approach to measure customers loyalty for this purpose a large data set from Kaggle based on customers reviews on online product is taken. POS tagging extract the textual and non-textual information of the reviews, pre-processes them, and converts this textual information into tokens. The proposed ANN approach generates vectors from pre-processed and mapped reviews. For the prediction of aspect-based sentiment, the trained dataset is used. The technique utilized specializes in figuring out polarity of the evaluations that may be positive, neutral and negative. These three categories will use to evaluate loyalty, different libraries will used to find similarities of these categories such as Sent WordNet, Stanford Core NLP, etc.

Keyphrases: Artificial Neural Networks, customer loyalty, data analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mubbera Sahar and Aqsa Saleem and Muhammad Usman Javeed and Waheed Ramay},
  title = {Aspect Based Intelligent System for Measuring Customer Loyalty},
  howpublished = {EasyChair Preprint no. 8592},

  year = {EasyChair, 2022}}
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