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
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“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
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Cited by
Sorted by citations
Items 401–431 of 431
Prediksi Tweet Netizen Menggunakan Random Forest, Decision Tree, Naïve Bayes, dan Ensemble Algorithm
Harahap, V. N., Irmayani, D., & Harahap, S. Z. (2021) · Jurnal Teknik Informatika UNIKA Santo Thomas · doi:10.54367/jtiust.v6i1.1274
Approaches of Authorship Verification
Ayman, S., Eisa, M., & Farouk, F. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020920692
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
Nazeer, I., Rashid, M. U., Gupta, S. K., & Kumar, A. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch010
Assessing Public Opinions of Products Through Sentiment Analysis
Ng, C. Y., Law, K. M. Y., & Ip, W. H. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch073
A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis
Abdelkader, M. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch020
Thakur, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4170991
“Performing a case study on different kinds of machine learning classifiers [87] to develop sentiment analysis approaches [88] to deduce the best machine learning classifier in terms of performance characteristics for sentiment analysis of tweets related to exoskeleton technology.”
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Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles
Frye, R. H., & Wilson, D. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16564-1_1
CSR Fraud and Consumer Opinions: What Simulated “tweets” Can Explain about Consumer Beliefs in CSR
Harrison, K., & Huang, L. (2023) · Journal of Sustainable Marketing · doi:10.51300/jsm-2023-85
Proposed combination method for ensemble classification technique based on majority voting
Salim, A., Yousif, M. J., Bader, S., & Sagheer, A. M. (2023) · AIP conference proceedings · doi:10.1063/5.0154218
Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis
Li, Q., Zhang, K., Sun, L., & Xia, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44204-9_40
Harnessing Advanced Classifiers for Sentiment Analysis on Augmented Tweets
Kubheka, P. P., & Owolawi, P. A. (2023) · International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) · doi:10.1109/etncc59188.2023.10284961
“The sentiment analysis can be done using supervised methods [7] [8] [9] [10] and lexicon-based methods.”
MethodNovo algoritmo ensemble para detecção de fraude em transações de cartão de crédito
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A Social Media System for detecting Child Predators
Kasar, V., Patil, D. H., Fernandes, S., Chillarge, A., & Abuj, V. (2023) · International Conference on Advanced Computing Technologies and Applications (ICACTA) · doi:10.1109/icacta58201.2023.10392579
“Therefore, there is a need for effective tools and methods to identify such offenders and prevent them from harming innocent victims [31].”
BackgroundA Social Recommendation Mechanism for Esports Team up
Hsieh, C.-Y., Liou, J.-H., & Li, Y.-M. (2024) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai63651.2024.00017
Unveiling personality traits through Bangla speech using Morlet wavelet transformation and BiG
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Tewari, N., Joshi, M., Budhani, S. K., Kandpal, V., Sangaiah, A. K., & Jeena, B. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895489
“It's not entirely unjustified to be concerned about how generative AI could affect objectivity and academic integrity; Lo [18] noted a number of significant issues in his analysis of the research about ChatGPT's effects on education.”
Evaluating the Recognition of Sentiments in Text on Social Media Utilizing Machine Learning
Kaur, P., Chaudhary, D., & Singh, J. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625502810
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Monster images in artificial intelligence generated film: evolution from myth to cyberspacePreprint
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Analyzing Twitter Sentiments: Comparative Evaluation of Classification Models Using Cloud
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Türkçe Kozmetik Ürün Yorumlarının Duygu Analizi ile Değerlendirilmesi
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Hybrid Machine Learning Emotion Extractor using Twitter Dataset
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“This problem has attracted significant interest across various domains, such as news [1] and tweets [2 ].”
Text‐Based Analysis of Twitter Data with Machine Learning Models
Malathy, N., Sharmila, G., Yuvarshini, R., & Lavanya, R. (2026) · doi:10.1002/9781394393060.ch7
Maʼlumotlarga asoslangan baholash yondashuvlarini ijtimoiy media tarmogʻidan foydalanish metodikasi
Pazilova, D. (2024) · Eurasian Journal of Academic Research · doi:10.70413/ejar-v04-i12-ps-175
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
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