Cited by 485Sentiment Analysis and Opinion MiningText and Document Classification TechnologiesHate Speech and Cyberbullying Detection

Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

Ghiassi, M., Skinner, J., Zimbra, D.·2013·Expert Systems with Applications

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2013.05.057

Cites

63 of 63 references verified0 citation errors · 0 not checkable

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  • The automation of sentiment detection on social networks has gained attention for various purposes.

    “The automation of sentiment detection on these social networks has gained attention for various purposes [18][19][20].”

    From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [18]

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Authors
Ghiassi, M., Skinner, J., & Zimbra, D.
Affiliations
  • Santa Clara University (US)
  • Amazon (United States) (US)
Corresponding author
Ghiassi, M.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2013-11
Volume
40
Issue
16
Pages
6266-6282
ISSN
0957-4174 (ISSN-L)
OpenAlex
W1975428268
Cited by
485
References
63
Citations by year
2026: 10 · 2025: 13 · 2024: 20 · 2023: 28 · 2022: 38 · 2021: 53 · 2020: 44 · 2019: 60 · 2018: 58 · 2017: 54 · 2016: 51 · 2015: 36 · 2014: 16 · 2013: 3
FWCI
36.50
Type
journal article
Language
en
Open access
closed
Locations
Expert Systems with Applications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Keywords
Twitter sentiment analysis, brand sentiment, sentiment lexicon, dynamic neural networks, support vector machine, text classification

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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.

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