情感梯度-改进熵增加的情感分析

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Fernando Cardoso Durier da Silva, Ana Cristina Bicharra Garcia, Sean Wolfgand Matsui Siqueira
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引用次数: 0

摘要

网络上的信息共享也导致了假新闻的兴起和传播。考虑到虚假信息通常是为了引发读者比简单事实更强烈的情感而写的,情绪分析被广泛用于检测假新闻。然而,讽刺、反讽甚至笑话都使用类似的写作风格,这使得区分假和事实变得更加困难。我们提出了一种新的假新闻分类器,它考虑了一组语言属性和消息中包含的情感梯度。情感分析方法的基础是给新闻贴上一个独特的标签,将整个消息缩小为一种感觉。我们从更广泛的角度来看待信息的情感表达,试图揭示信息可能带来的情感梯度。我们使用两个包含葡萄牙语文本的数据集来测试我们的方法:一个是公开的,另一个是我们创建的,其中包含从互联网上删除的最新新闻。虽然我们认为我们的方法是通用的,但我们对葡萄牙语进行了测试。我们的研究结果表明,情感梯度对假新闻分类性能有显著的正向影响。F-Measure达到了94%,我们的方法超过了现有的方法(我们的结果的p值小于0.05)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Gradient - Improving Sentiment Analysis with Entropy Increase
Information sharing on the Web has also led to the rise and spread of fake news. Considering that fake information is generally written to trigger stronger feelings from the readers than simple facts, sentiment analysis has been widely used to detect fake news. Nevertheless, sarcasm, irony, and even jokes use similarwritten styles, making the distinction between fake and fact harder to catch automatically. We propose a new fake news Classifier that considers a set of language attributes and the gradient of sentiments contained in a message. Sentiment analysis approaches are based on labelling news with a unique value that shrinks the entire message to a single feeling. We take a broader view of a message’s sentiment representation, trying to unravel the gradient of sentiments a message may bring. We tested our approach using two datasets containing texts written in Portuguese: a public one and another we created with more up-to-date news scrapped from the Internet. Although we believe our approach is general, we tested for the Portuguese language. Our results show that the sentiment gradient positively impacts the fake news classification performance with statistical significance. The F-Measure reached 94 %, with our approach surpassing available ones (with a p-value less than 0.05 for our results).
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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
15
审稿时长
8 weeks
期刊介绍: Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.
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