Cited by 276Sentiment Analysis and Opinion MiningComputational and Text Analysis MethodsText and Document Classification Technologies

Machine learning algorithms for social media analysis: A survey

T.K., Balaji, Annavarapu, Chandra Sekhara Rao, Bablani, Annushree·2021·Computer Science Review

Integrity signals

Registry

Crossref · 10.1016/j.cosrev.2021.100395

Cites

260 of 260 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

3 of the 263 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
T.K., B., Annavarapu, C. S. R., & Bablani, A.
Affiliations
  • Indian Institute of Technology Dhanbad (IN)
  • Indian Institute of Information Technology Sri City (IN)
Corresponding author
Annavarapu, Chandra Sekhara Rao
Venue
Computer Science Review
Publisher
Elsevier BV
Published
2021-05
Volume
40
Pages
100395
ISSN
1574-0137 (ISSN-L)
OpenAlex
W3136003884
Cited by
276
References
263
Citations by year
2026: 14 · 2025: 70 · 2024: 82 · 2023: 69 · 2022: 35 · 2021: 6
FWCI
16.97
Type
journal article
Language
en
Open access
closed
Locations
Computer Science Review (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
social media analytics, machine learning algorithms, social media data, big data processing, machine learning applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review