Naive Bayes and Entropy based Analysis and Classification of Humans and Chat Bots

Smys, S.; Wang, Haoxiang

doi:10.36548/jismac.2021.1.004

Cited by 37 publications

(2 citation statements)

References 15 publications

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“…For instance, at present, malware and spam are distributed by chatbots by using different media such as chat networks, online games, etc. A Naïve Bayes Classifier in combination with other classifiers, e.g., with an entropy classifier, is a useful tool for distinguishing between a human user and a chatbot that represents a threat [37]. Another example is in medicine by successfully identifying the symptoms of respective diseases [38,39] and in bots designed to conduct job interviews [40], etc.…”

Section: Discussionmentioning

confidence: 99%

LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

Lahoz-Beltrá

López

2021

Computers

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Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots.

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Section: Discussionmentioning

confidence: 99%

LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

Lahoz-Beltrá

López

2021

Computers

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“…At present, the number of social and networking sites such as Twitter, Facebook and Instagram have grown rapidly for various communication purposes [111]. Various authors performed text analysis on the test data collected from these websites for the research purpose and presented different predictions such as personality predictions, happiness, well-being, emotion mining and opinion mining [102,107]. But in sign language there is less content available for the research and thus this domain needs much more attention.…”

Section: Related Workmentioning

confidence: 99%

An automatic machine translation system for multi-lingual speech to Indian sign language

Dhanjal

Singh

2021

Multimed Tools Appl

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Sign language (SL) is the best suited communication medium for hearing impaired people. Even with the advancement of technology, there is a communication gap between the hearing impaired and hearing people. The aim of this research work is to bridge this gap by developing an automatic system that translates the speech to Indian Sign Language using Avatar (SISLA). The whole system works in three phases: (i) The first phase includes the speech recognition (SR) of isolated words for English, Hindi and Punjabi in speaker independent environment (ii) The second phase translates the source language into Indian Sign Language (ISL) (iii) HamNoSys based 3D avatar represents the ISL gestures. The four major implementation modules for SISLA include: requirement analysis, data collection, technical development and evaluation. The multi-lingual feature makes the system more efficient. The training and testing speech sample files for English (12,660, 4218), Hindi (12,610, 4211) and Punjabi (12,600, 4193) have been used to train and test the SR models. Empirical results of automatic machine translation show that the proposed trained models have achieved the minimum accuracy of 91%, 89% and 89% for English, Punjabi and Hindi respectively. Sign language experts have also been used to evaluate the sign error rate through feedback. Future directions to enhance the proposed system using non-manual SL features along with the sentence level translation has been suggested. Usability testing based on survey results confirm that the proposed SISLA system is suitable for education as well as communication purpose for hearing impaired people.

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Bot‐Based Process Triggering by Incoming E‐mails and Documents

Nalini,

Dhanraj,

Balusamy

et al. 2024

Hyperautomation for Next‐Generation Industries

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Naive Bayes and Entropy based Analysis and Classification of Humans and Chat Bots

Cited by 37 publications

References 15 publications

LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

An automatic machine translation system for multi-lingual speech to Indian sign language

Bot‐Based Process Triggering by Incoming E‐mails and Documents

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