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Understanding the Voice of Customers in Amazon Reviews: Comparison of Machine Learning models

  • Unique Paper ID: 172427
  • Volume: 11
  • Issue: 8
  • PageNo: 3098-3106
  • Abstract:
  • Sentiment analysis, text analysis, and stemming are the core research aspects of contemporary NLP. They expand a researcher’s toolkit in the form of additional approaches and tools to process unstructured data, generating objective insights. These methods help computers “understand” human speech, which is a beneficial skill due to people’s incredible ability to be subjective while sharing their thoughts. The internet is filled with content reflecting various subjective approaches while various people share data considering their personal points of view. It is difficult for people to find necessary information and discover the truth, especially about products, because companies do not know their customers fully. While processing product reviews helps one understand the public’s sentiment about a commodity, one should summarize all positive, neutral, and negative reviews due to their significant amounts. This work can be extended to cover more product review websites and look at more complex natural language processing features in the future. To ensure precision, the system retains only the words present in the dataset, filtering out any extraneous terms that do not contribute to the analysis.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 8
  • PageNo: 3098-3106

Understanding the Voice of Customers in Amazon Reviews: Comparison of Machine Learning models

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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