摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx记者从Babes-Bolyai大学发来的消息，研究表明：“自动欺骗检测是一项重要的任务，在人类直接的物理交流和计算机介导的交流中都有许多应用。本文的目的是研究欺骗语言的本质。”我们的新闻记者从Babes-Bolyai University的研究中获得了一句话:“这项研究的主要目标是调查罗马尼亚文字通信中的欺骗行为。”我们创建了许多人工智能模型(基于支持向量机、随机森林、为了评价罗尼亚语语言查询和词数(LIWC)分类的有效性，我们对基于LIWC、TF-IDF和LSA的多种文本表示方法进行了对比，结果表明，在具有共同主题的数据集中，例如关于友谊的数据集，语言查询和词数(LIWC)分类的效率较高。摘要:利用tf-idf或LSA等一般文本表示进行文本分类是比较成功的。该方法的分类准确率达到91.3%,优于文献中的同类方法。这些发现对语言学和观点挖掘等领域有一定的指导意义。如有必要用英语以外的语言对本课题进行研究。收稿日期：2024年4月29日。201 0数学学科分类。68T50.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Babes-Bolyai University by NewsRx correspondents, research stated, "Automatic deception detection is an imp ortant task with several applications in both direct physical human communicatio n, as well as in computer-mediated one. The objective of this paper is to study the nature of deceptive language." Our news reporters obtained a quote from the research from Babes-Bolyai Universi ty: "The primary goal of this study is to investigate deception in Romanian writ ten communication. We created a number of artificial intelligence models (based on Support Vector Machine, Random Forest, and Artificial Neural Network) to dete ct dishonesty in a topic-specific corpus. To assess the efficiency of the Lingui stic Inquiry and Word Count (LIWC) categories in Romanian, we conducted a compar ison between multiple text representations based on LIWC, TF-IDF, and LSA. The r esults show that in the case of datasets with a common subject such as the one w e used regarding friendship, text categorization is more successful using genera l text representations such as TF-IDF or LSA. The proposed approach achieves an accuracy of the classification of 91.3%, outperforming the similar approaches presented in the literature. These findings have implications in fiel ds like linguistics and opinion mining, where research on this subject in langua ges other than English is necessary. Received by the editors: 29 April 2024. 201 0 Mathematics Subject Classification. 68T50."
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