Skip to main content
null
Scholarly Review Journal
  • Menu
  • Articles
    • Advocacy
    • Architecture
    • Art
    • Artificial Intelligence
    • Astronomy
    • Biology
    • Business
    • Chemistry
    • Computer Science
    • Crime
    • Economics
    • Education
    • Engineering
    • Environment
    • Ethics
    • Fashion
    • History
    • Law
    • Literature
    • Machine Learning
    • Mathematics
    • Media
    • Medicine
    • Mental Health
    • Music
    • Mythology
    • Philosophy
    • Political Science
    • Politics
    • Psychology
    • Religion
    • Sociology
    • Sport
    • Technology
    • All
  • For Authors
  • Editorial Board
  • About
  • Issues
  • Publication Calendar
  • Contact
  • AI Policy
  • IRB Policy
  • search

RSS Feed

Enter the URL below into your favorite RSS reader.

http://localhost:29378/feed
Machine Learning
Vol. Summer 2024, Issue 9, 2024June 15, 2024 CEST

Determining the Most Accurate Text Classifier Model for Predicting Whether Online Product Reviews are Human or Computer Generated

Leonard Collomb,
Text ClassificationTF-IDF VectorizationProduct ReviewsComputer-generated TextScikit-learnText ClassifierLogistic RegressionSupport Vector Machine (SVM)K-Nearest Neighbors ClassifierDecision Tree Classifier
Copyright Logoccby-4.0 • https://doi.org/10.70121/001c.121720
Photo by Myriam Jessier on Unsplash
Scholarly Review Journal
Collomb, Leonard. 2024. “Determining the Most Accurate Text Classifier Model for Predicting Whether Online Product Reviews Are Human or Computer Generated.” Scholarly Review Journal Summer 2024 (9). https:/​/​doi.org/​10.70121/​001c.121720.
Save article as...▾

View more stats

This website uses cookies

We use cookies to enhance your experience and support COUNTER Metrics for transparent reporting of readership statistics. Cookie data is not sold to third parties or used for marketing purposes.

Powered by Scholastica, the modern academic journal management system