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
Registry
Cites
Statistics
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
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
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
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,307
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,689
Turney, P. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073153 · Cited by 2,354
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335
Thumbs up? Sentiment Classification using Machine Learning TechniquesPreprint
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205070 · Cited by 2,211
Ensemble Methods: Foundations and Algorithms
Zhou, Z. (2012) · Cited by 2,051
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. A. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,949
Neural Network Ensembles, Cross Validation, and Active Learning
Krogh, A., & Vedelsby, J. (1994) · Cited by 1,838
Twitter power: Tweets as electronic word of mouth
Jansen, B. J. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21149 · Cited by 1,766
Sentiment Analysis and Subjectivity
Bing, L. (2010) · Cited by 1,724
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of ReviewsPreprint
Turney, P. D. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0212032 · Cited by 1,584
Sentiment Analysis of Twitter Data
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011) · Cited by 1,565
(1972) · Elsevier eBooks · doi:10.1016/c2013-0-02458-9 · Cited by 1,463
A holistic lexicon-based approach to opinion mining
Ding, X., Liu, B., & Yu, P. S. (2008) · doi:10.1145/1341531.1341561 · Cited by 1,398
Named Entity Recognition in Tweets: An Experimental Study
Ritter, A., Clark, S., & Etzioni, O. (2011) · Cited by 1,182
Web data mining: exploring hyperlinks, contents, and usage data
(2012) · Choice Reviews Online · doi:10.5860/choice.49-2718 · Cited by 1,181
Sammut, C., & Webb, G. I. (2017) · Springer eBooks · doi:10.1007/978-1-4899-7687-1_100318 · Cited by 1,043
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1001 · Cited by 1,029
Diversity creation methods: a survey and categorisation
Gavin Brown, B., Wyatt, J., Harris, R., & Yao, X. (2004) · Information Fusion · doi:10.1016/j.inffus.2004.04.004 · Cited by 1,022
Feature hashing for large scale multitask learning
Weinberger, K. Q., Dasgupta, A., Langford, J. C., Smola, A. J., & Attenberg, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553516 · Cited by 928
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
Mohammad, S. M., & Turney, P. D. (2010) · NPARC · Cited by 882
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of TweetsPreprint
Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.6242 · Cited by 812
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Davidov, D., Tsur, O., & Rappoport, A. (2010) · Cited by 698
Error Correlation and Error Reduction in Ensemble Classifiers
Tumer, K., & Ghosh, J. (1996) · Connection Science · doi:10.1080/095400996116839 · Cited by 605
Semantic Sentiment Analysis of Twitter
Saif, H., He, Y., & Alani, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35176-1_32 · Cited by 525
Sentiment analysis of blogs by combining lexical knowledge with text classification
Melville, P., Gryc, W., & Lawrence, R. D. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557156 · Cited by 509
Characterizing debate performance via aggregated twitter sentiment
Diakopoulos, N. A., & Shamma, D. A. (2010) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1753326.1753504 · Cited by 472
Combining lexicon-based and learning-based methods for twitter sentiment analysis
Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011) · Cited by 440
Exploiting social relations for sentiment analysis in microblogging
Hu, X., Tang, L., Tang, J., & Liu, H. (2013) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2433396.2433465 · Cited by 389
Sentiment classification: The contribution of ensemble learning
Wang, G., Sun, J., Ma, J., Xu, K. S. J., & Gu, J. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.08.002 · Cited by 386
Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification
Jonathon, & Read (2005) · Cited by 374
Standifird, S. S. (2001) · Journal of Management · doi:10.1016/s0149-2063(01)00092-7 · Cited by 368
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
Read, J. (2005) · doi:10.3115/1628960.1628969 · Cited by 367
Liu, B. (2006) · Springer eBooks · Cited by 366
Analysis of decision boundaries in linearly combined neural classifiers
Tumer, K., & Ghosh, J. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00085-2 · Cited by 334
Learning from Bullying Traces in Social Media
Xu, J., Jun, K.-S., Zhu, X., & Bellmore, A. D. (2012) · Cited by 332
Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph
Speriosu, M., Sudan, N., Upadhyay, S., & Baldridge, J. (2011) · Cited by 313
A theoretical and experimental analysis of linear combiners for multiple classifier systems
Fumera, G., & Roli, F. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.109 · Cited by 312
Shamma, D. A., Kennedy, L. S., & Churchill, E. F. (2009) · doi:10.1145/1631144.1631148 · Cited by 300
Large-scale machine learning at twitter
Lin, J., & Kolcz, A. (2012) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2213836.2213958 · Cited by 231
Cheong, M., & Lee, V. C. S. (2010) · Information Systems Frontiers · doi:10.1007/s10796-010-9273-x · Cited by 210
Hash Kernels for Structured Data
Shi, Q., PettersonJames, Dror, G., Langford, J. C., & Smola, A. J. (2009) · ANU Open Research (Australian National University) · doi:10.5555/1577069.1755873 · Cited by 207
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold.
Saif, H., Fernández, M., He, Y., & Alani, H. (2013) · Open Research Online - ORO (The Open University) · Cited by 206
Twitter Sentiment Analysis: A Bootstrap Ensemble Framework
Hassan, A., Abbasi, A., & Zeng, D. D. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.56 · Cited by 148
Polikar, R. (2009) · Scholarpedia · doi:10.4249/scholarpedia.2776 · Cited by 140
Guerra, P., Veloso, A., Meira, W., & Almeida, V. A. F. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020438 · Cited by 134
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
Bravo-Márquez, F., Mendoza, M., & Poblete, B. (2013) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2502069.2502071 · Cited by 129
Affect Analysis of Web Forums and Blogs Using Correlation Ensembles
Abbasi, A., Chen, H., Thoms, S., & Fu, T. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.51 · Cited by 126
Synthesis Lectures on Human Language Technologies
Dagan, I., Roth, D., Sammons, M., Zanzotto, F. M., Construction, W. C., Schäfer, R., & Bildhauer, F. (2013) · Cited by 122
Bakliwal, A., Arora, P., Madhappan, S., Kapre, N., Singh, M. P., & Varma, V. (2012) · Cited by 90
An effective statistical approach to blog post opinion retrieval
He, B., Macdonald, C., He, J., & Ounis, I. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458223 · Cited by 79
Sentiment Mining Using Ensemble Classification Models
Whitehead, M., & Yaeger, L. (2009) · Springer eBooks · doi:10.1007/978-90-481-3658-2_89 · Cited by 75
Extremely fast text feature extraction for classification and indexing
Forman, G., & Kirshenbaum, E. (2008) · doi:10.1145/1458082.1458243 · Cited by 71
Innovations and Advances in Computer Sciences and Engineering
Nabende, P., Tiedemann, J., Nerbonne, J., & Sobh, T. (2010) · Springer eBooks · doi:10.1007/978-90-481-3658-2 · Cited by 65
Small Statistical Models by Random Feature Mixing
Ganchev, K., & Dredze, M. H. (2008) · Cited by 62
Protein sequence classification using feature hashing
Caragea, C., Silvescu, A., & Mitra, P. (2012) · Proteome Science · doi:10.1186/1477-5956-10-s1-s14 · Cited by 49
Ensemble Learning for Sentiment Classification
Su, Y., Zhang, Y., Ji, D., Wang, Y., & Wu, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36337-5_10 · Cited by 43
If you are happy and you know it... tweet
Asiaee, A., Tepper, M., Banerjee, A., & Sapiro, G. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398481 · Cited by 40
Hutchison, D., Mattern, F., Takeo Kanade, A., Kleinberg, J. M., Nierstrasz, O., Pandu Rangan, C., Vardi, M. Y., Sudan, M., Mitchell, J. C., Tygar, D., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02326-2 · Cited by 33
Combining a large sentiment lexicon and machine learning for subjectivity classification
Lu, B., & Tsou, B. K. (2010) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2010.5580672 · Cited by 32
Bayes Error Rate Estimation Using Classifier Ensembles
Tumer, K., & Ghosh, J. (2003) · International Journal of Smart Engineering System Design · doi:10.1080/10255810305042 · Cited by 27
SwatCS: Combining simple classifiers with estimated accuracy
Clark, S., & Wicentwoski, R. (2013) · Cited by 23
Protein Sequence Classification Using Feature Hashing
Caragea, C., Silvescu, A., & Mitra, P. (2011) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2011.91 · Cited by 22
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities
Rodriguez-Penagos, C. G., Atserias, J., Codina, J., Narbona, D. G., Grivolla, J., Lambert, P., & Sauri, R. S. (2013) · Cited by 18
Sentiment Polarity Classification Using Statistical Data Compression Models
Ziegelmayer, D., & Schräder, R. (2012) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2012.43 · Cited by 16
Liu, P., & Su, Q. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45185-0 · Cited by 11
Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Wu, J., & Coggeshall, S. (2012) · Chapman & Hall/CRC eBooks · Cited by 10
Flagged first, then by citations
3 of the 88 references couldn’t be matched to a known paper.
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.
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
