miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests: papers linking to it

Kaggle · www.kaggle.com/datasets/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests (https://www.kaggle.com/datasets/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests in their Crossref records, the first in 2025; most of them (2) in 2026.
By category: AI (1), Finance (1) and Economics (1); the journals with the most are Neural Computing and Applications (1), The North American Journal of Economics and Finance (1) and Computational Economics (1).

By year#

20252026

Papers linking to miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests by publication year
Year	Papers
2025	1
2026	2

Journals#

Journals whose papers link to miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests most
Journal	Papers
Neural Computing and Applications	1
The North American Journal of Economics and Finance	1
Computational Economics	1

Papers linking to miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-01-01	An early prediction model on systemic risk under global risk: Using FinBERT and temporal fusion transformer to multimodal data fusion framework	The North American Journal of Economics and Finance	Xiao Jin, Shu-Ling Lin	13	cites it
2026-02-25	Enhancing Financial Forecasting with Dynamic Window Optimization, Sentiment Analysis, and Convolutional Transformers	Computational Economics	Dilip Kumar Sharma, Ravi Prakash Varshney, Anuradha Pillai et al.	1	cites it
2026-05-27	Predicting stock price movements using hyperbolic space representation learning with cross-attention and multimodal data fusion	Neural Computing and Applications	Tatsuya Fukasawa, Yuri Murayama, Kiyoshi Izumi	0	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-miguelaenlle-massive-stock-news-analysis-db-for-nlpbacktests

@misc{scholar4ai-code-kaggle-miguelaenlle-massive-stock-news-analysis-db-for-nlpbacktests,
  author = {{Scholar4AI}},
  title = {miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-miguelaenlle-massive-stock-news-analysis-db-for-nlpbacktests}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).