On Validity of Sentiment Analysis Scores and Development of Classification Model for Student-Lecturer Comments Using Weight-based Approach and Deep Learning

Ochilbek Rakhmanov
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引用次数: 3

Abstract

In this paper, a novel state-of-art classification method was presented for student-lecturer comment classification. Tf-Idf was used to assign weights for each word and several different ANN structures were tested. A large dataset, 52571 comments, was used during training. The results show that developed models clearly overperformed existing classification models in this field. 97% of prediction accuracy was achieved on 3-class dataset, while the prediction accuracy for 5-class dataset was 92%.
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基于权重方法和深度学习的情感分析分数有效性及师生评论分类模型开发
本文提出了一种新的师生评论分类方法。使用Tf-Idf为每个单词分配权重,并测试了几种不同的ANN结构。在训练期间使用了52571条评论的大数据集。结果表明,所建立的分类模型明显优于现有的分类模型。3类数据集的预测准确率为97%,5类数据集的预测准确率为92%。
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