Predicting the customer’s opinion on amazon products using selective memory architecture-based convolutional neural network

  • Trupthi MandhulaEmail author
  • Suresh Pabboju
  • Narsimha Gugulotu


Opinion mining and sentiment analysis are useful to extract subjective information out of bulk text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. Though performing sentiment analysis is a challenging task for the researchers to identify the user’s sentiments from the large datasets, it is unstructured in nature, and also includes slangs, misspells, and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; they are data collection, pre-processing, keyword extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, pre-processing was carried out for enhancing the quality of collected data. The pre-processing phase comprises of three systems: lemmatization, review spam detection, and removal of stop words and URLs. Then, an effective topic modelling approach latent Dirichlet allocation along with modified possibilistic fuzzy C-Means was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative, and neutral) by applying an effective machine learning classifier: Selective memory architecture-based convolutional neural network. The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6–20% related to the existing systems.


Convolutional neural network Latent Dirichlet allocation Lemmatization Modified possibilistic fuzzy c-means Adam optimization algorithm Sentiment analysis 



This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Trupthi Mandhula
    • 1
    Email author
  • Suresh Pabboju
    • 2
  • Narsimha Gugulotu
    • 3
  1. 1.Information TechnologyChaitanya Bharathi Institute of TechnologyHyderabadIndia
  2. 2.Deptartment of Information TechnologyCBITGandipet, HyderabadIndia
  3. 3.Computer Science Engineering, College of EngineeringJNTUHHyderabadIndia

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