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Chatbots for CRM and Dialogue Management Read chapter Read first chapter

2021 | OriginalPaper | Chapter

6. Truth, Lie and Hypocrisy

Author : Boris Galitsky

Published in: Artificial Intelligence for Customer Relationship Management

Publisher: Springer International Publishing

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Abstract

Automated detection of text with misrepresentations such as fake reviews is an important task for online reputation management. We form the Ultimate Deception Dataset that consists of customer complaints—emotionally charged texts, which include descriptions of problems customers experienced with certain businesses. Typically, in customer complaints, either customer describes company representative lying, or they lie themselves. The Ultimate Deception Dataset includes almost 3 000 complaints in the personal finance domain and provides clear ground truth based on available factual knowledge about the financial domain. Among them, four hundred texts were manually tagged. Experiments were performed in order to explore the links between implicit cues of the rhetoric structure of texts and the validity of arguments, and also how truthful/deceptive are these texts. We confirmed that communicative discourse trees are essential to detect various forms of misrepresentation in text, achieving 76% F1 on the Ultimate Deception Dataset. We believe that this accuracy is sufficient to assist a manual curation of a CRM environment towards having high-quality, trusted content. Recognizing hypocrisy in customer communication concerning his impression with the company or hypocrisy in customer attitude is fairly important for proper tackling and retaining customers. We collect a dataset of sentences with hypocrisy and learn to detect it relying on syntactic, semantic and discourse-level features and also web mining to correlate contrasting entities. The sources are customer complaints, samples of texts with hypocrisy on the web and tweets tagged as hypocritical. We propose an iterative procedure to grow the training dataset and achieve the detection F1 above 80%, which is expected to be satisfactory for integration into a CRM platform. We conclude this section with the detection of a rumor and misinformation in web document where discourse analysis is also helpful.

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Metadata
Title
Truth, Lie and Hypocrisy
Author
Boris Galitsky
Copyright Year
2021
Book
Artificial Intelligence for Customer Relationship Management

Print ISBN: 978-3-030-61640-3

Electronic ISBN: 978-3-030-61641-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-61641-0_6