Random Quote Generation by Category

Swati Gupta

ISBN 10: 6207468449 / ISBN 13: 9786207468447

Published by LAP LAMBERT Academic Publishing Feb 2024, 2024

About this Item

Neuware -In today's digital age, the generation of meaningful and contextually relevant quotes has become an intriguing area of exploration. The Quote Generation Project aims to leverage natural language processing (NLP) techniques and machine learning algorithms to create a dynamic and personalized quote generation system. Traditional approaches to quote generation often lack the ability to adapt to individual preferences and the ever-evolving linguistic landscape. This project addresses these limitations by incorporating advanced NLP models, implemented in Python, to enhance the quality and relevance of generated quotes.Unlike static quote databases, the proposed system utilizes machine learning to analyze user interactions and preferences, tailoring the generated quotes to align with individual tastes. The implementation includes features such as user feedback analysis to ensure personalised responses.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch.

Seller Inventory # 9786207468447

Bibliographic Details

• Title: Random Quote Generation by Category
• Publisher: LAP LAMBERT Academic Publishing Feb 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 6207468449
• ISBN 13: 9786207468447
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams

About this title

Synopsis

In today's digital age, the generation of meaningful and contextually relevant quotes has become an intriguing area of exploration. The Quote Generation Project aims to leverage natural language processing (NLP) techniques and machine learning algorithms to create a dynamic and personalized quote generation system. Traditional approaches to quote generation often lack the ability to adapt to individual preferences and the ever-evolving linguistic landscape. This project addresses these limitations by incorporating advanced NLP models, implemented in Python, to enhance the quality and relevance of generated quotes.Unlike static quote databases, the proposed system utilizes machine learning to analyze user interactions and preferences, tailoring the generated quotes to align with individual tastes. The implementation includes features such as user feedback analysis to ensure personalised responses.