jp797498e/twitter-entity-sentiment-analysis: papers linking to it

Kaggle · www.kaggle.com/datasets/jp797498e/twitter-entity-sentiment-analysis

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to jp797498e/twitter-entity-sentiment-analysis (https://www.kaggle.com/datasets/jp797498e/twitter-entity-sentiment-analysis), 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 jp797498e/twitter-entity-sentiment-analysis in their Crossref records, the first in 2024; most of them (1) in 2026.
By category: AI (3); the journals with the most are Knowledge and Information Systems (2) and Knowledge-Based Systems (1).

By year#

20242026

Papers linking to jp797498e/twitter-entity-sentiment-analysis by publication year
Year	Papers
2024	1
2025	1
2026	1

Journals#

Journals whose papers link to jp797498e/twitter-entity-sentiment-analysis most
Journal	Papers
Knowledge and Information Systems	2
Knowledge-Based Systems	1

Papers linking to jp797498e/twitter-entity-sentiment-analysis, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-10-11	Twitter sentiment analysis using ensemble of multi-channel model based on machine learning and deep learning techniques	Knowledge and Information Systems	Jitendra Vikram Tembhurne, Kirtan Lakhotia, Anant Agrawal	18	cites it
2025-05-26	Aspect-based sentimental analysis using optimized multi-layered deep BiLSTM classifier	Knowledge-Based Systems	Lal Babu Purbey, Kamlesh Lakhwani	3	cites it
2026-05-07	Cross-lingual sentiment analysis via multimodal transformer fusion and lightweight deep ensemble learning framework	Knowledge and Information Systems	Rashmi Yadav, Deepak Kumar Yadav, Ati Jain et al.	1	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). jp797498e/twitter-entity-sentiment-analysis: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-jp797498e-twitter-entity-sentiment-analysis

@misc{scholar4ai-code-kaggle-jp797498e-twitter-entity-sentiment-analysis,
  author = {{Scholar4AI}},
  title = {jp797498e/twitter-entity-sentiment-analysis: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-jp797498e-twitter-entity-sentiment-analysis}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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