Kernel Metadata Declaration — on demand
ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.
✓ Meets every rule of the format
The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.
What the DOI identifies
Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis
10.46243/jst.2024.v9.i01.pp139-147 · JournalArticle — an article in a journal · Digital · Visual · Language · en
Published 2024-01-25
Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 9 · no. 1 · pp. 139–147
Principal agents
- Dr. Harsh Lohia (author → Author)
- Longman Publishers (publisher → Publisher)
Also in the record, outside the Kernel: the abstract, the licence, 3 links, 8 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name and the Handbook's (in grey), read off the record.
| DOI Name DOI name | 10.46243/jst.2024.v9.i01.pp139-147 |
| Referent Type referentType | Creation |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal |
| Referent Name(s) referentName(s) | Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis (PrincipalTitle, en) |
| Basic Metadata basicMetadata | author: Dr. Harsh Lohia publisher: Longman Publishers published: 2024-01-25 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 9 · no. 1 · pp. 139–147 language: en form: Digital · Visual · Language |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy |
| Created Date issueDate | 2024-02-16 |
| relatedIdentifiers | none needed — the descriptive metadata is in this record |
The record
{
"format": "smartscholars-doi-metadata/1.0",
"doi": "10.46243/jst.2024.v9.i01.pp139-147",
"referent": "Creation",
"type": "JournalArticle",
"structural_type": "Digital",
"modes": [
"Visual"
],
"characters": [
"Language"
],
"titles": [
{
"value": "Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis",
"type": "PrincipalTitle",
"lang": "en"
}
],
"identifiers": [
{
"type": "DOI",
"value": "10.46243/jst.2024.v9.i01.pp139-147"
}
],
"agents": [
{
"role": "author",
"name": {
"given": "Dr. Harsh",
"family": "Lohia"
},
"sequence": "first"
},
{
"role": "publisher",
"name": {
"org": "Longman Publishers"
}
}
],
"dates": {
"published": "2024-01-25",
"date_type": "PublicationDate",
"online": "2024-01-25"
},
"language": "en",
"container": {
"type": "Journal",
"titles": [
{
"value": "Journal of Science & Technology",
"type": "PrincipalTitle"
}
],
"identifiers": [
{
"type": "ISSN",
"value": "2456-5660",
"medium": "electronic"
}
],
"volume": "9",
"issue": "1",
"pages": {
"first": "139",
"last": "147"
}
},
"links": [
{
"url": "https://www.jst.org.in/index.php/pub/article/view/48",
"return_type": "text/html",
"primary": true
},
{
"url": "https://www.jst.org.in/index.php/pub/article/download/48/39",
"purpose": "text-mining",
"return_type": "application/pdf"
},
{
"url": "https://www.jst.org.in/index.php/pub/article/download/48/1472",
"purpose": "text-mining",
"return_type": "application/xml"
}
],
"abstract": {
"value": "Dual Sentiment Analysis has emerged as a crucial and active research field. It involves extracting sentiment from comments, feedback, or critiques, which serves as valuable indicators for various purposes. To address this, we propose a novel dual training algorithm that utilizes both original and reversed training reviews to develop a robust sentiment classifier. Additionally, we introduce a dual prediction algorithm that comprehensively assesses both aspects of a review for classification during testing. The proposed approach goes beyond traditional polarity (positive-negative) classification by extending the framework to a 3-class system, which includes neutral reviews. This enhancement allows for a more nuanced understanding of sentiment. By considering neutral reviews, we gain deeper insights into the sentiment landscape. Dual Sentiment Analysis plays a pivotal role in helping companies gauge the level of acceptance of their products and formulate strategies to improve product quality. Moreover, it empowers policymakers and politicians to gain valuable insights by analyzing public sentiments on policies, public services, and political issues.",
"lang": "en"
},
"license": {
"url": "https://creativecommons.org/licenses/by/4.0",
"start": "2024-01-25",
"applies_to": "vor"
},
"references": [
{
"key": "ref1",
"doi": "10.1109/icicct.2017.7975207",
"unstructured": "Shivaprasad T K and Jyothi Shetty, “Sentiment Analysis of Product Reviews: A Review”, International Conference on Inventive Communication and Computational Technologies (ICICCT 2017)"
},
{
"key": "ref2",
"doi": "10.1109/tkde.2011.48",
"unstructured": "Chenghua Lin, Yulan He, Richard Everson and Stefan Ruger, “Weakly Supervised Joint Sentiment-Topic Detection from Text”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 6, JUNE 2012"
},
{
"key": "ref3",
"doi": "10.1109/tkde.2015.2407371",
"unstructured": "Rui Xia, FengXu, Chengqing Zong, Qianmu Li, Yong Qi, and Tao Li, “Dual Sentiment Analysis: Considering Two Sides of One Review”, IEEE TRANSACTIONS ON JOURNAL NAME, MANUSCRIPT ID, Citation information: DOI 10.1109/TKDE 2015. 2407371"
},
{
"key": "ref4",
"unstructured": "Bing Liu, “Sentiment Analysis and Opinion Mining”, http://www.morganclaypool.com / toc/hlt/1/1, Copyright ©2012 by Morgan & Claypool, DOI: 10.2200/ S00416ED1V01Y 201204HLT016, ISSN 1947-4040"
},
{
"key": "ref5",
"doi": "10.1145/1361684.1361685",
"unstructured": "Abbasi, A. H. Chen, and A. Salem, “Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums,” ACM Trans. Information Systems, vol. 26, no. 3, pp. 1-34, 2008.digitaluniverse"
},
{
"key": "ref6",
"doi": "10.1109/tkde.2010.110",
"unstructured": "A. Abbasi, S. France, Z. Zhang, and H. Chen, “Selecting attributes for sentiment classification using feature relation networks,” IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 23, no. 3, pp. 447-462, 2011"
},
{
"key": "ref7",
"unstructured": "I. Councill, R. MaDonald, and L. Velikovich, “What’s Great and What’s Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis,” Proceedings of the Workshop on negation and speculation in natural language processing, pp. 51-59, 2010"
},
{
"key": "ref8",
"unstructured": "J. Li, G. Zhou, H. Wang, and Q. Zhu, “Learning the Scope of Negation via Shallow Semantic Parsing,” Proceedings of the International Conference on Computational Linguistics (COLING), 2010"
}
],
"record": {
"registrant": "Longman Publishers",
"registered": "2024-02-16",
"updated": "2026-09-17",
"issue_number": 1,
"source": "crossref-api",
"source_agency": "Crossref (member 25296)"
}
}The Kernel Metadata Declaration
DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation
<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
<referentDoiName>10.46243/jst.2024.v9.i01.pp139-147</referentDoiName>
<primaryReferentType>Creation</primaryReferentType>
<registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
<issueDate>2026-10-04</issueDate>
<issueNumber>1</issueNumber>
<referentCreation>
<name primaryLanguage="en">
<value>Identifying Product Aspect Polarity by Product Review Classification with Dual Sentiment Analysis</value>
<type>PrincipalTitle</type>
</name>
<identifier>
<nonUriValue>10.46243/jst.2024.v9.i01.pp139-147</nonUriValue>
<uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2024.v9.i01.pp139-147</uri>
<type>DOI</type>
</identifier>
<identifier>
<uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/48</uri>
<type>URI</type>
</identifier>
<identifier>
<uri>https://www.jst.org.in/index.php/pub/article/download/48/39</uri>
<type>URI</type>
</identifier>
<identifier>
<uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/48/1472</uri>
<type>URI</type>
</identifier>
<structuralType>Digital</structuralType>
<mode>Visual</mode>
<character>Language</character>
<type>JournalArticle</type>
<principalAgent>
<name>
<value>Dr. Harsh Lohia</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Longman Publishers</value>
<type>PrincipalName</type>
</name>
<role>Publisher</role>
</principalAgent>
<linkedCreation>
<name>
<value>Journal of Science & Technology</value>
<type>PrincipalTitle</type>
</name>
<identifier>
<nonUriValue>2456-5660</nonUriValue>
<type>ISSN</type>
</identifier>
<referentCreationRole>Part</referentCreationRole>
<referentCreationSequenceIdentifier>
<value>9</value>
<type>VolumeNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>1</value>
<type>IssueNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>139-147</value>
<type>PageNumber</type>
</referentCreationSequenceIdentifier>
</linkedCreation>
<language>en</language>
<languageOfReferentContent>
<language>en</language>
<languageOfReferentContentType>Original</languageOfReferentContentType>
</languageOfReferentContent>
<creationDate>
<date>2024-01-25</date>
<creationDateType>PublicationDate</creationDateType>
</creationDate>
</referentCreation>
</kernelMetadata>
