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  <doi_batch_id>aspg-21-3170-1791472113</doi_batch_id>
  <timestamp>20261008150833</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>International Journal of Neutrosophic Science</full_title>
    <abbrev_title>IJNS</abbrev_title>
    <issn media_type="print">2692-6148</issn>
    <issn media_type="electronic">2690-6805</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>25</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Sentimental Analysis to Predict Stock Market Using in Neutrosophic Time Series</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Saravanaraj .S</given_name>
      <surname>.S</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Vediyappan</given_name>
      <surname>Govindan</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Mathematics, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Said</given_name>
      <surname>Broumi</surname>
      <affiliations>
       <institution>
        <institution_name>Laboratory of Information Processing, Faculty of Science Ben M'Sik, University, Hassan II, B.P 7955, Morocco</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Haewon</given_name>
      <surname>Byeon</surname>
      <affiliations>
       <institution>
        <institution_name>Department of AI Big data, Inje University, Gimhae, 50834, Republic of Korea</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This study delves into the innovative use of sentiment analysis in conjunction with neutrosophic time series to forecast stock market trends in various contexts. By meticulously analyzing financial news and social media data, sentiment scores are derived and subsequently integrated into a neutrosophic time series model. This model is uniquely adept at managing uncertainty and indeterminacy, providing a robust framework for prediction. The findings indicate that this integrated approach significantly enhances predictive accuracy and reliability over traditional time series models. This research presents a novel methodology for tackling the intrinsic unpredictability of stock markets, offering a more reliable tool for investors and analysts across diverse financial environments. Additionally, by incorporating sentiment scores from a wide range of sources, the model captures a comprehensive view of market sentiment, reflecting the collective mood and opinions of investors. This comprehensive approach ensures that the predictions are not only accurate but also reflective of real-time market dynamics. Finally, this work highlights the possibility of merging sentiment analysis with sophisticated modeling approaches to change stock market prediction, as well as providing a promising avenue for future financial forecasting research.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>176</first_page>
     <last_page>182</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3170</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/IJNS.250215</doi>
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