stdClass Object
(
    [id] => 17095
    [paper_index] => 202507-02-023255
    [title] => FAKE REVIEW IDENTIFICATION THROUGH TOPIC MODELING AND SUPERVISED LEARNING
    [description] => 
    [author] => Md Sirajul Huque, Lavanya kumari.Ch, Gundekar Ashwini
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra23255
    [year] => 2025
    [month] => July
    [volume] => 10
    [issue] => 7
    [file] => fm/jpanel/upload/2025/July/202507-02-023255.pdf
    [abstract] => Online reviews significantly influence consumer purchasing decisions and serve as a vital source of public opinion on products and services. These reviews not only help customers make informed choices but also provide businesses with constructive feedback to improve their offerings. However, the increasing reliance on online reviews has led to the rise of opinion spam—intentionally deceptive reviews created to falsely promote or criticize a product for personal or competitive gain [1]. This manipulation undermines trust in digital platforms, making it essential to detect and filter out fake reviews. Since manually identifying deceptive content is both time-consuming and inefficient, automated methods powered by Natural Language Processing (NLP) and machine learning have become necessary [2][3][4]. NLP enables the extraction of key linguistic features from review texts, revealing subtle differences in language usage between authentic and fake reviews [3][5]. Fraudulent reviewers often use exaggerated or emotionally charged language to influence readers, creating distinguishable patterns in word choice and topic focus [3][6]. By leveraging these linguistic and structural differences, machine learning models such as Support Vector Machines, Random Forests, and neural networks can be trained to classify reviews as genuine or fake [7][8][9][10]. This study presents a model that utilizes such language-based cues to effectively distinguish deceptive content, ultimately enhancing the accuracy and performance of automated fake review detection systems [2][4].
    [keywords] => Fake Review Detection, Opinion Spam, Deceptive Reviews, Natural Language Processing (NLP), Machine Learning, Text Classification, Consumer Behavior
    [doj] => 2025-07-26
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 42
    [journal_id] => 2
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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