UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2006101


Registration ID:
233916

Page Number

749-753

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Title

CONTEXTUAL FLOW CHAT BOT FOR BUSINESS DATA ANALYSIS USING NATURAL LANGUAGE PROCESSING

Abstract

We present dell digital, The Speech and textual information play a crucial role in communicating between humans and conversational assistance. A conversational agent (chatbot) is a piece of software that can communicate with human using natural language (NL) or free flow language (FFL). There is a need for a system that respond to various queries in real-time. According to AI and machine learning algorithms, chat-bots are forecasted to bring forth accuracy, precision and availability of information when used. Several smart services have been deployed, bundled with information retrieval system, and other digital services for responding to human queries in closed domain. So, the Jarvis is the contextual assistant which responds to the business queries. Here, Rasa Platform is used, where Rasa NLU is an open-source natural language processing tool for intent classification, response retrieval and entity extraction in chat bots. As, the rasa nlu was separate library, now it is part of the rasa framework. Here, the conversational agent uses the LSTM algorithm where it comes under the deep learning algorithm’s where SVM algorithm is for the normal generic bots. The precision and accuracy of Jarvis contextual assistant is about is about 88.0% in getting response, the trained models generated is about 95 %, the created dialogue flow will assist the company for providing the related information in accurate way and Responses with valid information of the world-wide regions business ups and downs.

Key Words

Chatbot, Machine Learning, Artificial Intelligence, Natural Language, LSTM Algorithm, Contextual Assistant, Information Retrieval System, TensorFlow, Data Mining.

Cite This Article

"CONTEXTUAL FLOW CHAT BOT FOR BUSINESS DATA ANALYSIS USING NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.749-753, June-2020, Available :http://www.jetir.org/papers/JETIR2006101.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"CONTEXTUAL FLOW CHAT BOT FOR BUSINESS DATA ANALYSIS USING NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp749-753, June-2020, Available at : http://www.jetir.org/papers/JETIR2006101.pdf

Publication Details

Published Paper ID: JETIR2006101
Registration ID: 233916
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 749-753
Country: KGF,KOLAR, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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