Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier

Version 1 : Received: 19 July 2023 / Approved: 19 July 2023 / Online: 19 July 2023 (10:13:27 CEST)

A peer-reviewed article of this Preprint also exists.

Duanzhu, S.; Wang, J.; Jia, C. Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier. Fractal Fract. 2023, 7, 744. Duanzhu, S.; Wang, J.; Jia, C. Hotel Comment Emotion Classification Based on the MF-DFA and Partial Differential Equation Classifier. Fractal Fract. 2023, 7, 744.

Abstract

In this paper, we propose a partial differential equation model based on phase-field for sentiment analysis in the field of hotel comment texts. The comment texts are converted into word vectors using the Word2Vec tool, and then we utilize the multifractal detrended fluctuation analysis (MF-DFA) model to extract the generalized Hurst exponent of the word vector time series to achieve dimensionality reduction of the word vector data. The dimensionality reduced data is represented in a two-dimensional computational domain, and the modified Allen-Cahn (AC) function is used to evolve the phase values of the data to obtain a stable nonlinear boundary, thereby achieving automatic classification of hotel comment texts. The experimental results show that the proposed method can effectively classify positive and negative samples and achieve excellent results in classification indicators. We compared our proposed classifier with traditional machine learning models and the results indicate that our method possesses a better performance.

Keywords

emotion classification; MF-DFA; Hurst exponent; Allen-Cahn

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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