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Intent Classification in Conversational System using Machine Learning Techniques

by Monica Menda, G. Satya Keerthi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 51
Year of Publication: 2022
Authors: Monica Menda, G. Satya Keerthi
10.5120/ijca2022921913

Monica Menda, G. Satya Keerthi . Intent Classification in Conversational System using Machine Learning Techniques. International Journal of Computer Applications. 183, 51 ( Feb 2022), 6-11. DOI=10.5120/ijca2022921913

@article{ 10.5120/ijca2022921913,
author = { Monica Menda, G. Satya Keerthi },
title = { Intent Classification in Conversational System using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 51 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number51/32271-2022921913/ },
doi = { 10.5120/ijca2022921913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:28.419969+05:30
%A Monica Menda
%A G. Satya Keerthi
%T Intent Classification in Conversational System using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 51
%P 6-11
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The improvement of chatbots has ended up greater famous in current days. Many conversational chatbots were created for that reason so far. Chatbots are computer programs that speak with humans in herbal language. These chatbot structures were programmed with a little expertise to understand sentences and make a judgment as a reaction to a query. On the chatbot system, a class method was changed into offered on this studies to discover the reason, person input, or reason class. To decide the reason and examine it for every other, the Naive Bayes method, Logistic Regression method, and SVM have been used in this study. The quantity of accuracy, precision, recall, and f1-score are obtained, and don't forget of each method is the parameter utilized to assess the outcomes. Based on categorization outcome, we can decide which algorithm is great for a powerful bot primarily based totally on the metrics.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Chatbot Intent Classification Natural language Processing Multinomial Naïve Bayes SVM Logistic Regression.

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