<?xml version="1.0" encoding="UTF-8"?>
<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-information-and-optimization-sciences</journal-id>
      <journal-title-group>
        <journal-title>Journal of Information and Optimization Sciences</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0103</issn>
      <issn publication-format="print">0252-2667</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JIOS-1866</article-id>
      <title-group>
        <article-title>Benchmarking VADER and TextBlob for sentiment analysis : An evaluation across diverse textual domains in retail product reviews</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Kumar</surname>
            <given-names>Vijay</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Sehrawat</surname>
            <given-names>Nitu</given-names>
          </name>
          <aff>Department of Applied Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yadav</surname>
            <given-names>Anu</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Narula</surname>
            <given-names>Rachna</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Kapoor</surname>
            <given-names>Dishant</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Kumar</surname>
            <given-names>Anil</given-names>
          </name>
          <aff>Department of Applied Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Tyagi</surname>
            <given-names>Devansh</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Sharma</surname>
            <given-names>Ruchi</given-names>
          </name>
          <aff>Department of Computer Science, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, 110063, India</aff>
        </contrib>
      </contrib-group>
      <volume>46</volume>
      <issue>1</issue>
      <fpage>223</fpage>
      <lpage>232</lpage>
      <pub-date date-type="pub">
        <day>19</day>
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <abstract>
        <p>Sentiment analysis holds the potential to become an essential tool for businesses to understand how consumers feel about their products and services on digital platforms. For this study, we focused on two well-known sentiment analysis tools: VADER and TextBlob. Our results highlight the strengths and weaknesses of both tools and offer insights for researchers and practitioners looking to apply sentiment analysis in retail-related applications. We concluded that text analysis tools can be invaluable.</p>
      </abstract>
      <kwd-group>
        <kwd>VADER</kwd>
        <kwd>TextBlob</kwd>
        <kwd>Sentiment analysis</kwd>
        <kwd>Benchmarking</kwd>
        <kwd>Product reviews</kwd>
        <kwd>Textual domains</kwd>
        <kwd>Performance evaluation</kwd>
        <kwd>Sentiment nuances</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
