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Developing Nonlinear Customer Preferences Models for Product Design Using Opining Mining and Multiobjective PSO-Based

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This study introduces a new customer preference model using opinion mining and adaptive neuro-fuzzy inference system (ANFIS) optimized with multiobjective particle swarm optimization (PSO). This approach enhances product design by better analyzing online reviews.

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Area of Science:

  • Artificial Intelligence
  • Data Mining
  • Consumer Behavior Analysis

Background:

  • Online customer reviews offer valuable insights for product optimization but existing models struggle with incomplete product attributes, emotional fuzziness, and model complexity.
  • Traditional methods like adaptive neuro-fuzzy inference system (ANFIS) face challenges with large datasets and computational time.
  • Previous research has not fully addressed the nonlinearity and ambiguity inherent in customer emotions within online reviews.

Purpose of the Study:

  • To develop an improved customer preference model by integrating opinion mining with a multiobjective particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS).
  • To address the limitations of existing models in capturing product attributes, customer emotions, and computational efficiency.
  • To enhance the analysis of online customer reviews for more accurate customer preference modeling.

Main Methods:

  • Utilizing opinion mining to analyze customer preferences and product information from online reviews.
  • Implementing a multiobjective particle swarm optimization (PSO) algorithm to optimize the adaptive neuro-fuzzy inference system (ANFIS) for customer preference modeling.
  • Developing a novel approach for customer preference modeling based on the integrated opinion mining and PSO-ANFIS framework.

Main Results:

  • The proposed multiobjective PSO-based ANFIS effectively overcomes the inherent limitations of standard ANFIS, particularly in handling complex and large-scale datasets.
  • The integrated approach demonstrates superior performance in modeling customer preferences compared to traditional methods like fuzzy regression and genetic programming.
  • A case study on hair dryer reviews validated the effectiveness and accuracy of the proposed customer preference modeling technique.

Conclusions:

  • The integration of multiobjective PSO with ANFIS provides a robust and efficient method for customer preference modeling from online reviews.
  • This advanced approach significantly improves the analysis of customer sentiment and product attributes, leading to better product design and optimization.
  • The study highlights the potential of opinion mining and advanced computational intelligence techniques in understanding and modeling consumer behavior.