Automatic Detection of Verbal Deception in Romanian With Artificial Intelligence Methods
DOI:
https://doi.org/10.24193/subbi.2024.1.05Keywords:
Deception Detection, Text Classification, Natural Language Processing, Machine LearningAbstract
Automatic deception detection is an important task with several applications in both direct physical human communication, as well as in computer-mediated one. The objective of this paper is to study the nature of deceptive language. The primary goal of this study is to investigate deception in Romanian written communication. We created a number of artificial intelligence models (based on Support Vector Machine, Random Forest, and Artificial Neural Network) to detect dishonesty in a topic-specific corpus. To assess the efficiency of the Linguistic Inquiry and Word Count (LIWC) categories in Romanian, we conducted a comparison between multiple text representations based on LIWC, TF-IDF, and LSA. The results show that in the case of datasets with a common subject such as the one we used regarding friendship, text categorization is more successful using general 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 fields like linguistics and opinion mining, where research on this subject in languages other than English is necessary.
Received by the editors: 29 April 2024.
2010 Mathematics Subject Classification. 68T50.
1998 CR Categories and Descriptors. I.2.7 ARTIFICIAL INTELLIGENCE.: Nat- ural Language Processing –Text analysis.
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