Paper Title :Hybrid Models for Scraping and Natural Language Processing In Large-Scale Text Analysis for Calculating Market Value
Author :Vishwa Bandita Jena, Ritika Pal, Priyanshi Sharma, Mansi Garg, Ritika Kumari, Poonam Bansal
Article Citation :Vishwa Bandita Jena ,Ritika Pal ,Priyanshi Sharma ,Mansi Garg ,Ritika Kumari ,Poonam Bansal ,
(2024 ) " Hybrid Models for Scraping and Natural Language Processing In Large-Scale Text Analysis for Calculating Market Value " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 96-102,
Volume-12,Issue-7
Abstract : This research paper explores the integration of hybrid models comprising web scraping and natural language
processing (NLP) techniques for large-scale text analysis aimed at calculating market value. Leveraging web
scraping, which efficiently extracts vast amounts of data from online sources, enables swift access to a plethora of
information, facilitating tasks such as records retrieval, news gathering, internet monitoring, and marketing research.
Particularly in industries like electronics and automobiles, where product reviews and customer feedback are pivotal,
web scraping offers a rapid and systematic means of data extraction compared to manual methods. Additionally,
this paper investigates the fusion of hybrid models, incorporating Bidirectional Encoder Representations from
Transformers (BERT) and Long Short- Term Memory (LSTM) architectures, within the framework of web
scraping and natural language processing (NLP) for large-scale text analysis to estimate market value. Using web
scraping facilitates the efficient extraction of vast datasets from online sources, enabling comprehensive insights into
consumer sentiments, market trends, and brand perceptions. Leveraging BERT and LSTM models enhances the
depth and accuracy of sentiment analysis by capturing nuanced contextual information from textual data. Specifically,
the study explores how these advanced machine learning models can be applied to real-time news data to discern and
cat- egorize opinions expressed across various online platforms, aiding personalities and brands in understanding their
market positioning and filling critical gaps in their strategies. By integrating supervised learning techniques with web
scrap- ing methodologies, the proposed approach aims to optimize market value estimation by providing actionable
insights into consumer preferences, thereby enabling informed decision- making and strategic planning for
personalities and brands alike.
Type : Research paper
Published : Volume-12,Issue-7
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21104
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Copyright: © Institute of Research and Journals
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Published on 2024-10-17 |
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