Building Business Intelligence Data Extractor using NLP and Python


Authors : Tamilselvan Arjunan

Volume/Issue : Volume 7 - 2022, Issue 9 - September

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3rHIR1y

DOI : https://doi.org/10.5281/zenodo.7171791

The goal of the Business Intelligence data extractor (BID- Extractor) tool is to offer high-quality, usable data that is freely available to the public. To assist companies across all industries in achieving their objectives, we prefer to use cutting-edge, businessfocused web scraping solutions. The World wide web contains all kinds of information of different origins; some of those are social, financial, security, and academic. Most people access information through the internet for educational purposes. Information on the web is available in different formats and through different access interfaces. Therefore, indexing or semantic processing of the data through websites could be cumbersome. Web Scraping/Data extracting is the technique that aims to address this issue. Web scraping is used to transform unstructured data on the web into structured data that can be stored and analyzed in a central local database or spreadsheet. There are various web scraping techniques including Traditional copy-andpaste, Text capturing and regular expression matching, HTTP programming, HTML parsing, DOM parsing, Vertical aggregation platforms, Semantic annotation recognition, and Computer vision webpageanalyzers. Traditional copy and paste is the basic and tiresome web scraping technique where people need to scrap lots of datasets. Web scraping software is the easiest scraping technique since all the other techniques except traditional copy and pastes require some form of technical expertise. Even though there are many webs scraping software available today, most of them are designedto serve one specific purpose. Businesses cannot decide using the data. This research focused on building web scraping software using Python and NLP. Convert the unstructured data to structured data using NLP. We can also train the NLP NER model. The study's findings provide a way to effectively gauge business impact. The solution has a greater impact when applied to: Analyzing companies’ fundamentals Analyzing better deal opportunities.

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