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SRM-CSR: unsupervised aspect category detection based on semantic-aware relevance modeling and contextual sentence representation.

Code ↔ Paper

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The 1 match
  1. [1] § Experiments › Implementation details ↔ code/preprocess.py, lines 6–12 · score 0.75 · CountVectorizer, WordNetLemmatizer, lemmatized, stopword, tokenizing, English

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 51 lines · 1.8 KB · Apache-2.0 · 1 match

  1. from sklearn.feature_extraction.text import CountVectorizer
  2. from nltk.corpus import stopwords
  3. from nltk.stem.wordnet import WordNetLemmatizer
  4. import codecs
  6. def parseSentence(line):
  7. lmtzr = WordNetLemmatizer()
  8. stop = stopwords.words('english')
  9. text_token = CountVectorizer().build_tokenizer()(line.lower())
  10. text_rmstop = [i for i in text_token if i not in stop]
  11. text_stem = [lmtzr.lemmatize(w) for w in text_rmstop]
  12. return text_stem
  14. def preprocess_train(domain):
  15. f = codecs.open('../datasets/'+domain+'/train.txt', 'r', 'utf-8')
  16. out = codecs.open('../preprocessed_data/'+domain+'/train.txt', 'w', 'utf-8')
  18. for line in f:
  19. tokens = parseSentence(line)
  20. if len(tokens) > 0:
  21. out.write(' '.join(tokens)+'\n')
  23. def preprocess_test(domain):
  24. # For restaurant domain, only keep sentences with single
  25. # aspect label that in {Food, Staff, Ambience}
  27. f1 = codecs.open('../datasets/'+domain+'/test.txt', 'r', 'utf-8')
  28. f2 = codecs.open('../datasets/'+domain+'/test_label.txt', 'r', 'utf-8')
  29. out1 = codecs.open('../preprocessed_data/'+domain+'/test.txt', 'w', 'utf-8')
  30. out2 = codecs.open('../preprocessed_data/'+domain+'/test_label.txt', 'w', 'utf-8')
  32. for text, label in zip(f1, f2):
  33. label = label.strip()
  34. if domain == 'restaurant' and label not in ['Food', 'Staff', 'Ambience']:
  35. continue
  36. tokens = parseSentence(text)
  37. if len(tokens) > 0:
  38. out1.write(' '.join(tokens) + '\n')
  39. out2.write(label+'\n')
  41. def preprocess(domain):
  42. print '\t'+domain+' train set ...'
  43. preprocess_train(domain)
  44. print '\t'+domain+' test set ...'
  45. preprocess_test(domain)
  47. print 'Preprocessing raw review sentences ...'
  48. preprocess('restaurant')
  49. preprocess('beer')

preprocess.py at commit 1b17f1f, under Apache-2.0 · at the source

Overview

Authors: Yao Xu1,2, Xian Mu1,3, Ketong Liu1, Dagang Li1,4
  1. School of Computer Science and Engineering, Macau University of Science and Technology, Macau, 999078 China
  2. School of Economics, Fuyang Normal University, Fuyang, 236041 China
  3. College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang, 330044 China
  4. Zhuhai M.U.S.T. Science and Technology Research Institute, Zhuhai, 519000 China
Institutions: Macau University of Science and Technology (Macao SAR China); Fuyang Normal University (China); Jiangxi University of Water Resources and Electric Power (China); Nanchang Institute of Technology (China)
Journal: Scientific reports, volume 16, issue 1, article 24980
Dates: received 25 January 2026; accepted 23 May 2026; published online 1 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-55299-x · PMID 42225841 · PMCID PMC13462333 · OpenAlex W7163022393
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: methods / tools (subfield)
Methods: Machine learning
Keywords: Aspect category detection, Aspect-relevant terms, Contextual sentence representation, Pseudo-labeling, Engineering, Mathematics and computing
Topic: Topic Modeling (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Fund for the Development of Science and Technology (FDCT) of Macau (0095/2023/RIA2)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

ruidan/unsupervised-aspect-extraction

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1b17f1f65d7d562fc25b9538ee0238ec9da32df7, 9 December 2019
Languages: Python (10), Shell (1)
Size: 16 files, 11 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Keras (5 files), NumPy (4 files), scikit-learn (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/ruidan/Unsupervised-Aspect-Extraction?tab=readme-ov-file

teapot123/JASen

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c9dc8720ce3edbe16e5dd67f1be3c63b54b30580, 22 October 2020
Languages: Python (2), C (2), Shell (1)
Size: 16 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (2 files), NumPy (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/teapot123/JASen

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-55299-x.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 1 funder, 10 references.

Cite

This paper

Xu, Y., Mu, X., Liu, K., & Li, D. (2026). SRM-CSR: unsupervised aspect category detection based on semantic-aware relevance modeling and contextual sentence representation. Scientific reports, 16(1), 24980. https://doi.org/10.1038/s41598-026-55299-x

BibTeX

@article{xu2026srm,
author = {Xu, Yao and Mu, Xian and Liu, Ketong and Li, Dagang},
title = {{SRM-CSR: unsupervised aspect category detection based on semantic-aware relevance modeling and contextual sentence representation}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {24980},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-55299-x},
url = {https://doi.org/10.1038/s41598-026-55299-x},
pmid = {42225841},
pmcid = {PMC13462333}
}

RIS

TY - JOUR
AU - Xu, Yao
AU - Mu, Xian
AU - Liu, Ketong
AU - Li, Dagang
TI - SRM-CSR: unsupervised aspect category detection based on semantic-aware relevance modeling and contextual sentence representation
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/01
VL - 16
IS - 1
SP - 24980
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-55299-x
UR - https://doi.org/10.1038/s41598-026-55299-x
LA - en
ER -

CSL-JSON

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"container-title": "Scientific reports",
"author": [
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"PMCID": "PMC13462333",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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