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Memetic Spider Monkey Optimization for Spam Review Detection Problem.

Sayar Singh Shekhawat1, Harish Sharma1, Sandeep Kumar2

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|June 21, 2021
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Summary
This summary is machine-generated.

A new Memetic Spider Monkey Optimization (MeSMO) algorithm enhances local search for complex problems. MeSMO effectively detects spam reviews, improving precision by 3.68% in big data applications.

Keywords:
big datagolden section searchmemetic searchoptimizationspam reviewswarm intelligence

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Area of Science:

  • Computational Intelligence
  • Machine Learning
  • Data Mining

Background:

  • Spider Monkey Optimization (SMO) is a swarm-based algorithm inspired by social behavior, effective for complex problems.
  • SMO's exploration can be enhanced for improved local search capabilities.
  • Spam reviews significantly impact consumer decisions and business reputation.

Purpose of the Study:

  • To develop a hybridized Spider Monkey Optimization algorithm (MeSMO) with improved local search.
  • To apply the MeSMO algorithm to the challenging big data problem of spam review detection.

Main Methods:

  • Hybridizing the Spider Monkey Optimization algorithm with a memetic search strategy.
  • Implementing a MeSMO-based clustering approach for spam review classification.
  • Testing the MeSMO algorithm on four diverse spam review datasets.

Main Results:

  • The proposed Memetic SMO (MeSMO) algorithm demonstrated superior performance in spam review detection.
  • MeSMO achieved a precision improvement of 3.68% compared to existing state-of-the-art methods.
  • Comparative analysis confirmed MeSMO's effectiveness on complex spam datasets.

Conclusions:

  • The Memetic SMO algorithm is a robust and efficient technique for tackling the spam review detection problem.
  • Hybridizing SMO with memetic search significantly enhances its local search ability and overall performance.
  • MeSMO offers a promising solution for big data challenges in online review analysis.