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An improved random forest order review classification method based on Word2vec

Published: 27 January 2023 Publication History

Abstract

In recent years, with the continuous development of the Internet, online shopping has gradually become a mainstream shopping method for people. At the same time, people often comment on the purchased goods, which expresses their shopping feelings on the one hand and provides corresponding reference for others' shopping on the other. In order to better mine and analyze the massive review data and extract valuable reviews from them, this paper proposes an improved random forest classification method based on Word2vec, which optimizes the number of decision trees and the number of leaf node variables in the random forest algorithm by incorporating the seagull algorithm to classify the crawled e-mall review data emotionally. The results show that the word2vec+ISOA-RF classification model is improved more significantly and can effectively enhance the performance of the classification algorithm.

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ICIIP '22: Proceedings of the 7th International Conference on Intelligent Information Processing
September 2022
367 pages
ISBN:9781450396714
DOI:10.1145/3570236
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2023

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Author Tags

  1. Comment Classification
  2. ISOA-RF
  3. Random Forest
  4. Word2vec

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ICIIP '22

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Overall Acceptance Rate 87 of 367 submissions, 24%

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