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Imbalanced sentiment classification of online reviews based on SimBERT

Journal

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 45, Issue 5, Pages 8015-8025

Publisher

IOS PRESS
DOI: 10.3233/JIFS-230278

Keywords

Sentiment classification; imbalance classification; deep learning; BERT; SimBERT

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This article presents a method to address the issue of imbalanced sample distribution in sentiment classification of online reviews. By training and using the SimBERT model in the experiment, fake samples are generated and mixed with the original samples to obtain a balanced dataset, thereby improving the classification performance of the model.
The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods.

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