A Text Mining using Web Scraping for Meaningful Insights

Autor:	Kishor Kumar Reddy C, Nhu Gia Nguyen, P R Anisha, G Sreelatha
Rok vydání:	2021
Předmět:	World Wide Web History Text mining Computer science business.industry computer.software_genre business computer Web scraping Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 2089:012048
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/2089/1/012048
Popis:	This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b97c0ac1c6e1c4339de09153c26aa2fa https://doi.org/10.1088/1742-6596/2089/1/012048 Zobrazit plný text záznamu