The DOI matches its Crossref record; 85 of the 88 references it cites were found in a registry or an index.
Tweet sentiment analysis with classifier ensembles
Overview
Integrity signals
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Cites
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For DATA_SET 2, Logistic Regression Classifier gives the highest accuracy of 75% with bigrams under the Tfidf Vectorizer [58].”
From Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media (2020) · cites this paper as [58]
Citations
Cites
LIBSVM: A library for support vector machines
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Advances in neural information processing systems 7
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WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Combining Pattern Classifiers: Methods and Algorithms
Bagui, S. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 4,364
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.2200/s00416ed1v01y201204hlt016 · Cited by 3,995
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Peter, P., & Turney (2002) · Cited by 3,646
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388
Research works printed but not linked: 5
- A. Balahur, R. Steinberger, M. Kabadjov, V. Zavarella, E. van der Goot, M. Halkia, B. Pouliquen, J. Belyaeva, Sentiment analysis in the news, in: N. C. C. Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, D. Tapias (Eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), European Language Resources Association (ELRA), Valletta, Malta, 2010.
- Ekman, 1982, vol. 2
- 2012, Heterogeneous ensemble learning for Chinese sentiment classification, Journal of Information and Computational Science, 9, 4551
- Abbasi, 2008, Affect analysis of web forums and blogs using correlation ensembles, IEEE Transactions on Knowledge and Data Engineering, 20, 1168, 10.1109/TKDE.2008.51 · DOI
- Nakov, 2013, Semeval-2013 task 2: sentiment analysis in twitter, vol. 2, 312
Cited by
Sorted by citations
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
“The authors in Da Silva et al. (2014) applied voting-based four baseline classifiers (SVM, RF, and LR) using several English tweets datasets.”
Ensemble of keyword extraction methods and classifiers in text classification
Onan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.045 · Cited by 630
Enhancing deep learning sentiment analysis with ensemble techniques in social applications
Araque, Ó., Corcuera, I., Sánchez-Rada, J. F., & Iglesias, C. Á. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.002 · Cited by 564
Giachanou, A., & Crestani, F. (2016) · ACM Computing Surveys · doi:10.1145/2938640 · Cited by 562
“On the contrary, da Silva et al. [2014] employed more classifiers compared to Lin and Kolcz [2012].”
ResultDeep Convolution Neural Networks for Twitter Sentiment Analysis
Zhao, J., Xiaolin, G., & Zhang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2776930 · Cited by 498
Metadata
- Authors
- da Silva, N. F. F., Hruschka, E. R., & Hruschka, E. R.
- Affiliations
- Universidade de São Paulo (BR)
- ORCID
- Hruschka, Eduardo R.
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 66
- Pages
- 170-179
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2003303386
- Cited by
- 431
- References
- 88
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 14 · 2023: 37 · 2022: 50 · 2021: 52 · 2020: 69 · 2019: 47 · 2018: 60 · 2017: 41 · 2016: 38 · 2015: 11
- FWCI
- 29.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- Funders
- Research Agencies CAPES (DS-7253238/D)
- FAPESP (2013/07787-6 and 2013/07375-0)
- CNPq (303348/2013-5)
- 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, ensemble classifiers, lexicon-based sentiment classification, microblogging, Twitter, polarity classification, product search
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.
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