论文标题
基于神经网络的假新闻标题的分类
Classification Of Fake News Headline Based On Neural Networks
论文作者
论文摘要
在过去的几年中，文本分类是自然语言处理（NLP）的基本任务之一，其中目的是将文本文档分类为预定义的类别之一。这个消息充满了我们的生活。因此，新闻头条分类是将用户与正确的新闻联系起来的至关重要的任务。新闻标题分类是一种文本分类，通常可以分为三个部分：提取，分类器选择和评估。在本文中，我们使用数据集，其中包含Kaggle Platform提供的18年期间的新闻来对新闻头条进行分类。我们选择TF-IDF来提取特征和神经网络作为分类器，而评估指标是准确的。从实验结果来看，很明显，我们的NN模型在准确性指标中具有最佳性能。准确性越高，模型的性能就越好。我们的NN模型拥有准确性0.8622，这在这四个模型中的准确性最高。它是0.0134、0.033、0.080，比其他型号高0.080。
Over the last few years, Text classification is one of the fundamental tasks in natural language processing (NLP) in which the objective is to categorize text documents into one of the predefined classes. The news is full of our life. Therefore, news headlines classification is a crucial task to connect users with the right news. The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations. In this article, we use the dataset, containing news over a period of eighteen years provided by Kaggle platform to classify news headlines. We choose TF-IDF to extract features and neural network as the classifier, while the evaluation metrics is accuracy. From the experiment result, it is obvious that our NN model has the best performance among these models in the metrics of accuracy. The higher the accuracy is, the better performance the model will gain. Our NN model owns the accuracy 0.8622, which is highest accuracy among these four models. And it is 0.0134, 0.033, 0.080 higher than its of other models.
