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Sentiment Classification using N-gram IDF and Automated Machine Learning

2019-04-27 14:46:34
Rungroj Maipradit, Hideaki Hata, Kenichi Matsumoto

Abstract

We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering-related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning tool is used. In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.

Abstract (translated)

提出了一种基于通用机器学习框架的情绪分类方法。对于特征表示,n-gramIDF用于提取软件工程相关的、数据集特定的、正的、中性的和负的n-gram表达式。对于分类器,使用自动机器学习工具。在使用公开数据集进行比较时,我们的方法在所有数据集的正负句中都达到了最高的f1值。

URL

https://arxiv.org/abs/1904.12162

PDF

https://arxiv.org/pdf/1904.12162.pdf


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