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