Cited by 431Sentiment Analysis and Opinion MiningText and Document Classification TechnologiesHate Speech and Cyberbullying Detection

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

da Silva, Nádia F.F., Hruschka, Eduardo R., Hruschka, Estevam R.·2014·Decision Support Systems

DOI 10.1016/j.dss.2014.07.003

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.dss.2014.07.003

Cites

85 of 85 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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

Flagged first, then by citations

Show all 88 references

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

Sorted by citations

Items 201–300 of 431

An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words
Olaleye, T. O., Arogundade, O. '., Abayomi-Alli, A. A., & Adesemowo, A. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-824536-1.00004-6 · Cited by 12
SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis
Yenkikar, A., & Babu, C. N. (2023) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v11i1.4381 · Cited by 12
Movie aspects, tweet metrics, and movie revenues: The influence of iOS vs. Android
Zimbra, D. K., Sarangee, K. R., & Jindal, R. P. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.08.002 · Cited by 12
A Training‐Optimization‐Based Method for Constructing Domain‐Specific Sentiment Lexicon
Du, M., Li, X., & Luo, L. (2021) · Complexity · doi:10.1155/2021/6152494 · Cited by 12
Sentiment Analysis of Social Network Content to Characterize the Perception of Security
Sierra, L. F. C., Pulido, C., Rudas, J., Reyes, A. M. M., Victorino, J., Narvaez, L. A., Gómez, F. A., & Martínez, D. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381434 · Cited by 12
“For preprocessing and due to the nature of the tweets, links, mentions, and hashtags were removed [14].”
Method
Social Market: Stock Market and Twitter Correlation
Bernardo, I., Henriques, R., & Lobo, V. J. A. S. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-59424-8_32 · Cited by 11
Explaining short text classification with diverse synthetic exemplars and counter-exemplars
Lampridis, O., State, L., Guidotti, R., & Ruggieri, S. (2022) · Machine Learning · doi:10.1007/s10994-022-06150-7 · Cited by 13
“Deep Neural Networks (DNNs) Korde and Mahender (2012), Zhang et al. (2015) and Random Forests (RFs) da Silva et al. (2014) and Xu et al. (2012), have been shown to be effective in terms of predictive accuracy and robustness to noise.”
Background
Sentiment Analysis on Social Media
Wadawadagi, R. S., & Pagi, V. B. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch041 · Cited by 14
Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm
Osmani, A., & Bagherzadeh, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7612276 · Cited by 11
Enhancing sentiment analysis via fusion of multiple embeddings using attention encoder with LSTM
Soni, J., & Mathur, K. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02102-w · Cited by 12
Evaluating cross domain sentiment analysis using supervised machine learning techniques
Aziz, A. A., Starkey, A. J., & Bannerman, M. N. (2017) · Intelligent Systems Conference (IntelliSys) · doi:10.1109/intellisys.2017.8324369 · Cited by 11
“RF has been applied in many domains such as disaster management [16, 17], biomedical [29, 15] and computer science [14].”
Method
Employing sentiment analysis for gauging perceptions of minorities in multicultural societies: An analysis of Twitter feeds on the Afrikaner community of Orania in South Africa
Kotzé, E., & Senekal, B. (2018) · The Journal for Transdisciplinary Research in Southern Africa · doi:10.4102/td.v14i1.564 · Cited by 11
A crowd sensing system identifying geotopics and community interests from user-generated content
Tenney, M., Hall, G. B., & Sieber, R. (2019) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2019.1591413 · Cited by 11
Recommendation and Sentiment Analysis Based on Consumer Review and Rating
Ni, P., Li, Y., & Chang, V. I. C. (2020) · International Journal of Business Intelligence Research · doi:10.4018/ijbir.2020070102 · Cited by 11
Interpretable Sentiment Analysis based on Deep Learning: An overview
Jawale, S. S., & Sawarkar, D. S. D. (2020) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon50868.2020.9362361 · Cited by 12
“[8] “Machine learning methods for sentiment analysis” explored by Medhat et al.”
Method
A new sentiment classification method based on hybrid classification in Twitter
Minab, S. S., Jalali, M., & Moattar, M. H. (2015) · International Congress on Technology, Communication and Knowledge · doi:10.1109/ictck.2015.7582685 · Cited by 11
“They acknowledge that the group classification and words in the service of micro blogging in the past were not good enough and in order to optimize accuracy in the group classification, they studied on 1224 tweets by using four classification methods such as the naive bayesian, support vector machine, random tree and…”
Method
Smartphone-Assisted Pronunciation Learning Technique for Ambient Intelligence
Lee, J., Lee, C. H., Kim, D., & Kang, B. (2016) · IEEE Access · doi:10.1109/access.2016.2641474 · Cited by 10
Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information
Kim, N.-R., Kim, K., & Lee, J.-H. (2017) · International Journal of Fuzzy Logic and Intelligent Systems · doi:10.5391/ijfis.2017.17.4.329 · Cited by 10
“[21] showed poor performance of recall in the minority class, Positive.”
Background
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
Nazeer, I., Rashid, M. U., Gupta, S. K., & Kumar, A. (2020) · Advances in social networking and online communities book series · doi:10.4018/978-1-7998-4718-2.ch002 · Cited by 10
An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach
Alvi, M. B., Naeem, A., Mukhtiar, A., & Akram, M. S. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100674 · Cited by 10
HatEmoTweet: low-level emotion classifications and spatiotemporal trends of hate and offensive COVID-19 tweets
Adesokan, A., Madria, S., & Nguyen, L. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01132-6 · Cited by 10
On the Use of Emojis to Train Emotion ClassifiersPreprint
Hussien, W. A., Al‐Ayyoub, M., Tashtoush, Y. M., & Al-Kabi, M. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08906 · Cited by 10
“[24] examined the performance of two feature schemes, which are feature hashing and bag-of-words, in sentiment analysis on data from Twitter.”
Background
SPSO-EFVM: A Particle Swarm Optimization- Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis
Tiwari, D., Nagpal, B., Bhati, B. S., Gupta, M., Suanpang, P., Butdisuwan, S., & Nanthaamornphong, A. (2024) · IEEE Access · doi:10.1109/access.2024.3363158 · Cited by 9
Mining sentiment tendencies and summaries from consumer reviews
Ye, W.-J., & Lee, A. J. T. (2020) · Information Systems and e-Business Management · doi:10.1007/s10257-020-00482-4 · Cited by 9
“Silva et al. (2014) adopted an approach to classify the sentiment from tweets by using an ensemble classifier.”
BackgroundMethod
What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods
Huang, W., Liu, Y., Hu, P., Ding, S., Gao, S., & Zhang, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19525 · Cited by 11
“, which is a powerful tool for traditional statistical analysis [80].”
Method
Tweet Analysis Based on Distinct Opinion of Social Media Users'
Geetha, S., & Kaliappan, V. K. (2018) · International Conference on Soft-computing and Network Security (ICSNS) · doi:10.1109/icsns.2018.8573619 · Cited by 9
A Comprehensive Survey on Sentiment Analysis in Twitter Data
Krishnan, H., Elayidom, M. S., & Santhanakrishnan, T. (2022) · International Journal of Distributed Systems and Technologies · doi:10.4018/ijdst.300352 · Cited by 9
Sentiment Analysis for Microblog Related to Finance Based on Rules and Classification
Yan, D., Hu, B., & Qin, J. (2018) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2018.00026 · Cited by 9
Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis
Et.al, M. V. K. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i3.1639 · Cited by 9
The wisdom of the lexicon crowds: leveraging on decades of lexicon-based sentiment analysis for improved results
Hill, C., Fresneda, J. E., & Anandarajan, M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01186-7 · Cited by 8
“Machine learning ensembles are not within the scope of our work, however interested readers can refer to [5, 26, 34, 38, 96] for examples of machine learning ensemble models for sentiment analysis.”
Temporal and Sentimental Analysis of A Real Case of Fake Reviews in Taiwan
Wang, C., Day, M.-Y., Chen, C.-C., & Liou, J.-W. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3116206 · Cited by 8
“[22] analyzed Twitter posts using sentiment analysis.”
Sentiment Analysis on Aadhaar for Twitter Data—A Hybrid Classification Approach
Kumari, P., & Haider, M. T. U. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-0790-8_30 · Cited by 8
Quantifying Perception of Security Through Social Media and Its Relationship With Crime
Sierra, L. F. C., Pulido, C., Rudas, J., Victorino, J., Reyes, A. M. M., Estrada, C., Narvaez, L. A., & Gómez, F. A. (2021) · IEEE Access · doi:10.1109/access.2021.3114675 · Cited by 10
Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions
Thakur, N. (2022) · Analytics · doi:10.3390/analytics1020007 · Cited by 8
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations
Pant, S., Lim, E., Yang, H.-J., Lee, G.-S., Kim, S.-H., Kang, Y.-S., & 장혜림 (2022) · IEEE Access · doi:10.1109/access.2022.3221408 · Cited by 9
“Other applications of ERC include analyzing movie dialogues, instant messaging, tweets [7], [8], [9], and news headlines [10], [11].”
Background
Recent Developments in Sentiment Analysis on Social Networks: Techniques, Datasets, and Open Issues
Saxena, A., Reddy, H., & Saxena, P. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3398-0_13 · Cited by 8
A Review: Sentiment Analysis and Opinion MiningPreprint
Arya, A., Shukla, V., Negi, A., & Gupta, K. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3602548 · Cited by 8
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)
(2022) · doi:10.18653/v1/2022.woah-1 · Cited by 8
A Comprehensive Review on Text Classification and Text Mining Techniques Using Spam Dataset Detection
Siddiqui, T., & Amer, A. Y. A. (2023) · doi:10.1002/9781119896715.ch1 · Cited by 8
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
Singh, R., Gehlot, A., & Buddhi, D. (2022) · doi:10.13052/rp-9788770227667 · Cited by 9
Accurate frequency-based lexicon generation for opinion mining
Keshavarz, H., & Abadeh, M. S. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-16562 · Cited by 7
“[13, 26, 41] and baseline methods, such as [21].”
Influential
THU_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM
Wu, C., Wu, F., Liu, J., Yuan, Z., Wu, S., & Huang, Y. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1028 · Cited by 8
“Traditional Twitter sentiment analysis methods mainly focus on identifying the polarities (Da Silva et al., 2014; dos Santos and Gatti, 2014) or emotion categories (Dini and Bittar, 2016) of tweets.”
Method
A Systematic Review on Hidden Markov Models for Sentiment Analysis
Odumuyiwa, V., & Osisiogu, U. (2019) · International Conference on Electronics, Computer and Computation · doi:10.1109/icecco48375.2019.9043297 · Cited by 7
“The performance evaluation carried out with Health Care Reform (HCR) dataset with about 839 tweets shows that HMMs with 4 states in most cases outperformed HMM algorithms with lower states, other conventional machine learning methods like the SVM, NB and algorithms implemented in [26]–[28].”
Method
Recommendation and Sentiment Analysis Based on Consumer Review and Rating
Ni, P., Li, Y., & Chang, V. I. C. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6303-1.ch087 · Cited by 7
DERIV: distributed brand perception tracking framework
Shukla, M., Santos, R. D., Fong, A., & Lu, C. (2017) · Journal Of Big Data · doi:10.1186/s40537-017-0078-3 · Cited by 7
“Classifier ensembles have been explored for tweet sentiment analysis [17].”
Understanding Animal Behavior Using Their Trajectories
Ardakani, I., Hashimoto, K., & Yoda, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91131-1_1 · Cited by 7
Sentiment Analysis of Indonesians Response to Influencer in Social Media
Tauhid, S. M., & Ruldeviyani, Y. (2020) · International Conference on Information Technology, Computer, and Electrical Engineering · doi:10.1109/icitacee50144.2020.9239218 · Cited by 7
“Another research by Silva et al. is done to analyze a sentiment using an automatic approach with ensemble classification and lexicons [19].”
Method
Sentiment Analysis of Twitter Data Using Machine Learning and Deep Learning Methods
Manda, K. R. (2019) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 7
Content analysis of Chinese cities’ Five-Year Plan transport policy documents
Guo, M., Li, Q., Wu, C., Vine, S. L., & Ren, G. (2023) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2023.101055 · Cited by 10
Analyzing Social Trend Towards Girl Child in India: A Machine Intelligence-Based Approach
Hore, S., & Bhattacharya, T. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1280-9_4 · Cited by 6
Predicting hotel reviews from sentiment: a multinomial classification framework
YÜCEL, A., Çağlar, M., Dolatsara, H. A., George, B., & Dağ, A. (2021) · Journal of Modelling in Management · doi:10.1108/jm2-09-2020-0255 · Cited by 6
eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features
Ding, Z., Han, J., Qian, R., Shen, L., Chen, S., Yu, L., Zhu, Y., & Liu, R. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3324482 · Cited by 6
“The Bag-of-Words (BoW) model [14], [40] serves as a solid foundation for various works in domains such as text mining [41], text categorization [42], information retrieval and speech emotion recognition [43], among others.”
Background
Having Fun?: Personalized Activity-Based Mood Prediction in Social Media
Roshanaei, M., Han, R. Y., & Mishra, S. (2017) · Lecture notes in social networks · doi:10.1007/978-3-319-51049-1_1 · Cited by 6
Influencia de redes sociales en el análisis de sentimiento aplicado la situación Política en Ecuador
Gómez-Torres, E. R., Jaimes, R., Hidalgo, O., & Luján‐Mora, S. (2018) · Enfoque UTE · doi:10.29019/enfoqueute.v9n1.235 · Cited by 6
A Machine Learning Based Approach Towards Building a SustainabilityModel for NBA Players
Hore, S., & Bhattacharya, T. (2018) · International Conference Inventive Communication and Computational Technologies · doi:10.1109/icicct.2018.8473102 · Cited by 6
Tweet Analysis Based on Distinct Opinion of Social Media Users’
Geetha, S., & Kumar, K. V. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1882-5_23 · Cited by 6
A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental Analysis
Latif, M., & Qamar, U. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-22871-2_39 · Cited by 6
Improving sentiment analysis efficacy through feature synchronization
Ali, Z., Razzaq, A., Ali, S., Qadri, S., & Zia, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10383-w · Cited by 6
On smoothing and scaling language model for sentiment based information retrieval
Najar, F., & Bouguila, N. (2022) · Advances in Data Analysis and Classification · doi:10.1007/s11634-022-00522-6 · Cited by 6
Sentiment Analysis on Social Media
Wadawadagi, R. S., & Pagi, V. B. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-9643-1.ch024 · Cited by 6
A Comparison of Machine Learning and Deep Learning Methods with Rule Based Features for Mixed Emotion Analysis
Christy, D., & Loganathan, S. (2020) · International journal of intelligent engineering and systems · doi:10.22266/ijies2021.0228.05 · Cited by 6
“Most of the research work on sentiment classification using twitter dataset can be categorized with respect to the used supervised methods [20], based on training through linear classifiers like Random Forest, Naïve Byes, logistic regression, Maximum Entropy and Support Vector Machine utilizing…”
Method
Deep Learning for Sentiment Analysis in Financial Markets
Botta, A., Mohini, P., Raju, A., & Balaji, C. (2024) · International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst61793.2024.10578359 · Cited by 5
A city-wide examination of fine-grained human emotions through social media analysis
Siriaraya, P., Zhang, Y., Kawai, Y., Jeszenszky, P., & Jatowt, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279749 · Cited by 5
“) has been well explored in prior studies ([9, 34] etc.”
Result
Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data
Hai-Jew, S. (2016) · Advances in e-business research series · doi:10.4018/978-1-5225-0846-5.ch010 · Cited by 5
STACKING ENSEMBLE MODEL FOR POLARITY CLASSIFICATION IN FEATURE BASED OPINION MINING
Tribhuvan, P. P., Bhirud, S. G., & Deshmukh, R. R. (2018) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2018/v9i3/180903004 · Cited by 5
“This ensemble model constructed using four learners Support Vector Machine, Bayes Point Machine, Logistic Regression and Decision Forest [6].”
Influential
Particle Swarm Optimized Ensemble Learning for Enhanced Predictive Sentiment Accuracy of Tweets
Kumar, A., & Jaiswal, A. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-30577-2_56 · Cited by 5
Tweet Netizen Prediction Using Random Forest, Decision Tree, Naïve Bayes, And Ensemble Algorithm (Case Study The Governor Of DKI Jakarta)
Kuntoro, A. Y. (2020) · SinkrOn · doi:10.33395/sinkron.v5i1.10565 · Cited by 5
Sentiment Analysis of Tweets Using Machine Learning Algorithms
Pate, R., Patil, S., Patil, M., & Raut, R. (2023) · International Conference on Applied Intelligence and Sustainable Computing (ICAISC) · doi:10.1109/icaisc58445.2023.10199501 · Cited by 5
Generating effective ensembles for sentiment analysis
Etelis, I., Rosenfeld, A., Weinberg, A. I., & Sarne, D. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00963-0 · Cited by 9
Sentiment Mining Approaches for Big Data Classification and Clustering
Ashok Kumar, J., Abirami, S., & Trueman, T. E. (2017) · Advances in data mining and database management book series · doi:10.4018/978-1-5225-2805-0.ch002 · Cited by 5
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
Symeonidis, S., Kordonis, J., Effrosynidis, D., & Arampatzis, A. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2147 · Cited by 5
“Moreover, feature hashing was used as an approach according to (Da Silva et al., 2014 a learning algorithm.”
Method
Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction
Hao, S., Wang, P., & Hu, Y. (2019) · Information · doi:10.3390/info10020081 · Cited by 5
“In the comparative experiments, CNN [4], Fast R-CNN [29], and Faster R-CNN are deep learning methods, and naive Bayes [32], and SVM [33] are traditional machine learning methods.”
Method
Market Data Analysis by Using Support Vector Machine Learning Technique
Reddy, R., & Shyam, G. K. (2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-13-6459-4_3 · Cited by 5
A Sentiment Classification Model Based on Multiple Multi-classifier Systems
Yang, K., Liao, C., & Zhang, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24265-7_25 · Cited by 5
Supervised learning methods application to sentiment analysis
Altares-López, S., & Cuadrado-Gallego, J. J. (2019) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3331076.3331086 · Cited by 5
“Supervised learning methods are algorithms which base their learning process on an accurately labeled training data set [4].”
Method
Sentiment Analysis Tool for Spanish Tweets in the Ecuadorian Context
Utitiaj, I., Morillo, P., & Vallejo-Huanga, D. (2020) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3446132.3446424 · Cited by 5
“Both approaches are applied in [5] for polarity detection, through a”
Method
An Assessment Study of Features and Meta-Level Features in Twitter Sentiment Analysis
Carvalho, J., & Plastino, A. (2016) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-672-9-769 · Cited by 5
Automatic complaint classification system using classifier ensembles
Fauzi, M. A. (2018) · Telfor Journal · doi:10.5937/telfor1802123a · Cited by 5
Feature-based Sentiment Analysis for Slang Arabic Text
Abdallah, E. E., & Sarah, A. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100436 · Cited by 5
#CycloneGaja-Rank based Credibility Analysis System in social media during the crisis
Devi, P. S., & Karthika, S. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2020.01.064 · Cited by 5
Mining User’s Opinions and Emojis For Reputation Generation Using Deep Learning
Boumhidi, A., & Nfaoui, E. H. (2020) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds50568.2020.9268758 · Cited by 5
“Initially, sentiment analysis was carried out using conventional methods with core steps like sentence modeling using bag-of-words (BoW) [11].”
Method
Comparing SERVQUAL for Transportation Services in the Sharing Economy for Emerging Markets: Insights from Twitter Analytics
Bijarnia, S., Ilavarasan, P. V., & Kar, A. K. (2019) · Advances in theory and practice of emerging markets · doi:10.1007/978-3-030-24374-6_9 · Cited by 4
A Survey on Sentiment Analysis Techniques for Twitter
Verma, S., & Jain, A. K. (2021) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-8413-2.ch003 · Cited by 4
Extremely Randomized Tree Based Sentiment Polarity Classification on Online Product Reviews
Saranya, R. B., Kesavan, R., & Devi, K. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24094-2_11 · Cited by 4
Hybrid Sentiment Analysis: Majority Voting with Multinomial Naive Bayes and Logistic Regression on IMDB Dataset
Zakaria, Z., & Sunyoto, A. (2023) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact59844.2023.10455937 · Cited by 4
“3 illustrates an example of combination rules [41].”
Background
Twitter user’s Behavior and Events Affecting Their Mood
Jindal, S., Tyagi, A. K., & Sharma, K. (2020) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.c6121.029320 · Cited by 4
“In the research, [5] sentiments of tweets were classified in positive or negative using ensemble classifier.”
Method
Is Climate Change a Topic of Concern to Indians? Assessing and Predicting Sentiments Using Deep Learning Techniques
Jena, R. K. (2022) · Environment and Urbanization Asia · doi:10.1177/09754253221120614 · Cited by 4
“Many researchers have noticed the impact of data pre-processing (i.e., removal of stop words) in the sentiment analysis (Da Silva et al., 2014; Loper & Bird, 2002).”
Background
Emotion-based classification through fuzzy entropy-enhanced FCM clustering
Cardone, B., Martino, F. D., & Senatore, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91776-6.00010-5 · Cited by 4
An Effective Framework for Sentiment Analysis of Hindi Sentiments Using Deep Learning Technique
Shrivash, B. K., Verma, D. K., & Pandey, P. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10702-y · Cited by 4
“[2] introduced hybrid models that included SVM, Decision Tree, and Naive Bayes.”
Method
An ensemble approach for the identification and classification of crime tweets in the English language
Siddiqui, T., Hina, S., Asif, R., Ahmed, S., & Ahmed, M. N. (2023) · Computer Science and Information Technologies · doi:10.11591/csit.v4i2.p149-159 · Cited by 4
“Whereas [27] and [32] applied it in sentiment analysis.”
Background
DERIV: Distributed In-Memory Brand Perception Tracking Framework
Shukla, M., Fong, A., Santos, R. D., & Lu, C. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0069 · Cited by 4
Efficient Topic Detection System for Online Arabic News
Mohammed, M., & Marwa, A. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018916236 · Cited by 4
Sentiment Analysis Applied to the Popularity Level of the Ecuadorian Political Leader Rafael Correa
Hidalgo, O., Jaimes, R., Gómez-Torres, E. R., & Luján‐Mora, S. (2017) · International Conference on Information Systems and Computer Science (INCISCOS) · doi:10.1109/inciscos.2017.64 · Cited by 4
“En un estudio de [4] se presenta una categorización entre los niveles de análisis.”
BackgroundInfluential
EFTSA: Evaluation Framework for Twitter Sentiment Analysis
Alsaeedi, A. (2019) · Journal of Software · doi:10.17706/jsw.14.1.24-35 · Cited by 4
“Saif, He, and Alani [33] used the HCR dataset in their supervised machine learning Twitter sentiment classifiers. da Silva, Hruschka, and Hruschka [29] used HCR for assessing their ensemble method.”
MethodInfluential
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data
Rajendran, A. K., Zhang, C., & Abdul-Mageed, M. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2136 · Cited by 4
“Da Silva et al. (2014) exploit ensembles to boost the accuracy on twitter sentiment analysis.”
Novel Machine Learning Approach for Sentiment Analysis of Real Time Twitter Data with Apache Flume
Rashid, M. U., Hamid, M. A., Aḥmad, N., Rehman, M. B., & Yousuf, M. M. (2020) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc50313.2020.9315782 · Cited by 4
“N. F. D. Silva et al [6] in 2014 used Ensemble classifier to extract the sentiment of tweets.”
Method
Analisis Sentimen Penerapan Kurikulum Merdeka Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor Dengan Forward Selection
Darmawan, W., Faizal, M., Setianto, W., & Hapsoro, W. (2023) · Smart Comp Jurnalnya Orang Pintar Komputer · doi:10.30591/smartcomp.v12i1.4634 · Cited by 4
Combining User Contexts and User Opinions for Restaurant Recommendation in Mobile Environment
Liu, Q., & Xiaohong, G. (2016) · Journal of Electronic Commerce in Organizations · doi:10.4018/jeco.2016010105 · Cited by 4
Fashioning Data - A Social Media Perspective on Fast Fashion Brands
Chakraborty, R., Kundu, S., & Agarwal, P. (2016) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w16-0407 · Cited by 4
A Personality-Aware Followee Recommendation Model Based on Text Semantics and Sentiment Analysis
Xiao, P., Fan, Y., & Du, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_42 · Cited by 4
“In particular, the extraction of emotional words is mainly based on corpus and dictionary[16].”
Background

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)
DOI: 10.1016/j.dss.2014.07.003
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

Tweet sentiment analysis with classifier ensembles

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks