Comparison of the Performance of Pegasos and Traditional Models in the Task of Sentiment Classification of Product Reviews
Sentiment classification of a large number of commodity reviews is very important for customer selection and market trend prediction. The key to achieving high accuracy of sentiment classification is to select appropriate models for training. However, most of the existing research literature only us...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04019.pdf |
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| Summary: | Sentiment classification of a large number of commodity reviews is very important for customer selection and market trend prediction. The key to achieving high accuracy of sentiment classification is to select appropriate models for training. However, most of the existing research literature only uses traditional models or does not have a scientific comparison between models, and only provides training steps for a model that can be used for text sentiment analysis. Therefore, in this study, three models (two traditional models and one new model Pegasos) were selected for comparison. The same datasets and data preprocessing methods were selected for the three models to compare the final emotion classification accuracy, and finally, the experimental result with the highest accuracy of the Pegasos model was obtained. The study also analyzed the performance of different models in the experimental results and found the reasons for the poor performance of traditional models in handling sentiment classification tasks and the reasons for the good performance of Pegasos models. In the process of model hyperparameter tuning, the optimal number of iterations is 25 by comparing the classification accuracy under different iterations. |
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| ISSN: | 2271-2097 |