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CBVoSD: context based vectors over sentiment domain ensemble model for review classification

Abstract

With the exponential growth of e-commerce, billions of consumers can share their opinion in chat groups, retailer forums, private forums, or a review page on all kinds of products. Consequently, there is a significant rise in the number of online product reviews on e-commerce websites. However, buyers know less about the products or the seller’s credibility. Therefore, experienced buyers refer to historical reviews that can determine their purchasing decisions. The present research challenges are due to textual anomalies and sentiment expression variations. There are various approaches for evaluating sentiments, but word embedding strategies word2vec and GloVe transform words into meaningful vectors. However, these approaches neglect the word’s sentiment information. To generate an appropriate word vector based on sentiment analysis context, we proposed the context-based vectors over sentiment domain(CBVoSD) algorithm with ensemble model to collect the sentiment information, which improves sentiment classification accuracy. Due to their ever-changing dynamics, the suggested method identifies and categorizes sentiment toward products and services. The experiment results show that the CBVoSD ensemble method improves sentiment classification compared to state-of-the-art methods.

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Notes

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    https://github.com/MukulKirtiVerma/Negtive-Positive-Word-List.

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Correspondence to Chandra Sekhara Rao Annavarapu.

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Wankhade, M., Annavarapu, C.S.R. & Verma, M.K. CBVoSD: context based vectors over sentiment domain ensemble model for review classification. J Supercomput (2021). https://doi.org/10.1007/s11227-021-04132-5

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Keywords

  • Sentiment classification
  • Machine learning
  • Word embedding
  • Review analysis