Abstract: Web scraping is a powerful technique used to extract data from websites, and when combined with Python’s BeautifulSoup library, it becomes an efficient tool for data collection and analysis. BeautifulSoup simplifies the process of parsing HTML and XML documents, enabling users to navigate and extract the desired content with ease. In the context of sentiment analysis, web scraping plays a crucial role in gathering large volumes of text data from sources such as social media, review websites, and blogs. This data is then processed and analyzed to determine the sentiment—positive, negative, or neutral—using natural language processing (NLP) techniques. By leveraging Python’s BeautifulSoup, developers can automate data extraction, clean the collected data, and feed it into sentiment analysis models. This integration of web scraping and sentiment analysis provides valuable insights into public opinions, customer feedback, and market trends, making it a critical tool for businesses, researchers, and analysts in decision-making and strategy development.
Keywords: Webs craping, Python, BeautifulSoup, Data collection, HTML, XML
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DOI:
10.17148/IJARCCE.2025.14131
