Design a Chatbot for Chennai Corporation Using Logistic Regression Algorithm
Jeberson Retna Raj1, Salman2, Senduru Srinivasulu3
1Jeberson Retna Raj, Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Salman, Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Senduru Srinivasulu, Department of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 14 October 2019 | Revised Manuscript received on 23 October 2019 | Manuscript Published on 02 November 2019 | PP: 2320-2323 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B12600982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1260.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Chatbots are the famous nowadays in business because of its service offered to the community at large. They provide support of 24*7 for business in terms of customercare, helpline, planning, analyzing and decision making. In this paper, a chatbot for Chennai corporation is proposed. This chatbot helps the citizens in providing the responses for their queries related to civic problems. There is no such system is available to handle the public grievances automatically. This system handles the public query and the relevant suggestion and responses will be given promptly. The chatbot receives the text or voice input and processed. The voice recognition module used to recognize the voice query and the voice to text convertor used to convert the voice data into text format. The matchmaking process used to match the input query with the available data set and the relevant responses is generated. If no match is for the query, the matchmaker will find the relevant response from online sources. The output channel equipped with the text to voice converter which converts the text data into voice and it will be delivered to the end user. The naïve bayers and logistic regression algorithm is implemented for classifying the query and the performance is compared. The result shows that the logistic regression algorithm outperform well with the precision and recall values.
Keywords: Chatbot, NLP, Logistic Regression.
Scope of the Article: Algorithm Engineering