Web Scraping and Review Analytics. Extracting Insights from Commercial Data

Andreea-Maria Tanasa (), Simona-Vasilica Oprea () and Adela Bara ()
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Andreea-Maria Tanasa: The Bucharest University of Economic Studies, Romania
Simona-Vasilica Oprea: The Bucharest University of Economic Studies, Romania
Adela Bara: The Bucharest University of Economic Studies, Romania

Ovidius University Annals, Economic Sciences Series, 2023, vol. XXIII, issue 2, 370-379

Abstract: Web scraping has numerous applications. It can be used complementary with APIs to extract useful data from web pages. For instance, commercial data is abundant, but not always relevant as it is presented on websites. In this paper, we propose the usage of web scraping techniques (namely, two popular libraries – BeautifulSoup and Selenium) to extract data from web and other Python libraries and techniques (vaderSentiment, SentimentIntensityAnalyzer, nltk, n consecutive words) to analyze the reviews and obtain useful insights from this data. A web scraper is built in which prices are extracted and variations are tracked. Furthermore, the reviews are extracted and analyzed in order to identify the relevant opinions, including complaints of the customers.

Keywords: web scraping; price tracking; sentiment analysis; alerts (search for similar items in EconPapers)
JEL-codes: C55 C81 Z13 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xxiii:y:2023:i:2:p:370-379

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