Authors :
Adit Kulshreshtha; Ankit Sharma; Shameem Ahmad; Shubhanshu Pandey
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/27rzham9
Scribd :
https://tinyurl.com/bcvfteuj
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR890
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The software used in fake mobile applications
mimics the functions of real, reliable, and legitimate
applications. When these applications are released, they
frequently display advertisements, affect personal
information in the system, infect devices with viruses, etc.
to make money. It causes malicious behaviour such as
Most users cannot distinguish between real apps and fake
ones. That is why people always review user reviews
before installing an app. In this article, we introduce a
website where users can learn more about an application
before installing it. The results are based on previous
reviews and ratings provided by users and provide an
opportunity to determine the user experience for a
particular mobile application. More importantly, we will
evaluate the evaluation using emotional evaluation to
check whether the text is emotional, that is, whether the
text is emotional, i.e., whether the text is positive, negative,
or negative.
Keywords :
User Reviews, Sentiment Analysis, Lexicon, Tokenization, Stop Word Removal.
The software used in fake mobile applications
mimics the functions of real, reliable, and legitimate
applications. When these applications are released, they
frequently display advertisements, affect personal
information in the system, infect devices with viruses, etc.
to make money. It causes malicious behaviour such as
Most users cannot distinguish between real apps and fake
ones. That is why people always review user reviews
before installing an app. In this article, we introduce a
website where users can learn more about an application
before installing it. The results are based on previous
reviews and ratings provided by users and provide an
opportunity to determine the user experience for a
particular mobile application. More importantly, we will
evaluate the evaluation using emotional evaluation to
check whether the text is emotional, that is, whether the
text is emotional, i.e., whether the text is positive, negative,
or negative.
Keywords :
User Reviews, Sentiment Analysis, Lexicon, Tokenization, Stop Word Removal.
