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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]
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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
Big Data: Deep Learning for financial sentiment analysis
Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390
“Supervised classification methods, such as Support Vector Machines [48], Naïve Bayes [49] or ensembles [50, 51] have been deployed to perform sentiment analysis in multiple research projects.”
MethodChakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106754 · Cited by 375
“For example, cv_2 represents Count Vectorizer [58] with bigrams and tf_1 represents tfidf vectorizer is implemented with the unigram range.”
A Survey on ensemble learning under the era of deep learning
Yang, Y., Lv, H., & Chen, N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10283-5 · Cited by 406
“The obtained base deep learners were integrated to form the final ensemble deep learner through weighting by majority voting or meta-learning by random forests (Da Silva et al. 2014; Zhang and He 2015).”
MethodOnan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.06.005 · Cited by 369
Onan, A., Korukoğlu, S., & Bulut, H. (2017) · Information Processing & Management · doi:10.1016/j.ipm.2017.02.008 · Cited by 308
Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis
Naseem, U., Razzak, I., Musiał, K., & Imran, M. A. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.06.050 · Cited by 311
A Study on Sentiment Analysis Techniques of Twitter Data
Alsaeedi, A., & Zubair, M. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100248 · Cited by 284
“The ensemble method proposed in [38] attained accuracy scores of 76.”
BackgroundMethodInfluentialA multidisciplinary perspective of big data in management research
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2017) · International Journal of Production Economics · doi:10.1016/j.ijpe.2017.06.006 · Cited by 238
“Based on practical purposes, a lot of new methods are explored to detect emotions (e.g. Balahur et al., 2012; Gao et al., 2015), spot topics (e.g. Li and Wu, 2010), and improve sentiment classification accuracy (e.g. Colace et al., 2015; Khan et la., 2014; Da Silva et al., 2014).”
BackgroundAn Ensemble Classification System for Twitter Sentiment Analysis
Ankit, & Saleena, N. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.109 · Cited by 218
Analytical mapping of opinion mining and sentiment analysis research during 2000–2015
Piryani, R., Madhavi, D., & Singh, V. K. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.07.001 · Cited by 205
“…Kim, Ko, & Kim, 2014; Chung and Tseng, 2012; Clavel, 2015; Clavel et al., 2013; Cruz, 2012; Cruz et al., 2012; Cruz, Troyano, Pontes, & Ortega, 2014; Da Silva et al., 2014; Dai et al., 2015; Dang et al., 2010; del Pilar Salas-Zárate et al., 2014; Denecke and Deng, 2015; Z.-H. Deng et al.,…”
Stock market sentiment lexicon acquisition using microblogging data and statistical measures
Oliveira, N., Cortez, P. C., & Areal, N. M. D. P. B. D. C. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.02.013 · Cited by 188
“such as Support Vector Machines [3], Naive Bayes [4] or ensembles [5, 6].”
MethodRecognizing emotions in text using ensemble of classifiers
Perikos, I., & Hatzilygeroudis, I. (2016) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2016.01.012 · Cited by 176
Sentiment Analysis in Social Networks
Gonçalves, J. P. M. (2016) · Elsevier eBooks · doi:10.1016/c2015-0-01864-0 · Cited by 184
A survey on sentiment analysis and opinion mining for social multimedia
Li, Z., Fan, Y., Jiang, B., Lei, T., & Liu, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6445-z · Cited by 164
“[24] employed more classifiers such as RF, SVM and LR to automatically classify the sentiment of tweets, and explored different feature representation approaches like bag-of-words and feature hashing.”
MethodAn effective ensemble deep learning framework for text classification
Mohammed, A., & Kora, R. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.001 · Cited by 179
Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter
Abid, F., Alam, M., Yasir, M., & Chen, L. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.12.018 · Cited by 158
“The majority of the current work on Twitter sentiment classification can be clarified in the respect of supervised methods [9,10] which are based on training through linear classifier likewise, in Naïve Byes, Support Vector Machine, Random Forest, Maximum Entropy and logistic regression utilizing feature mixtures such…”
MethodSentiment analysis: a review and comparative analysis over social media
Singh, N. K., Tomar, D. S., & Sangaiah, A. K. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0862-8 · Cited by 151
“…analysis has been investigated mainly at three levels document level (Montejo-Rez et al. 2014a; Bravo-Marquez et al. 2014; Ortigosa et al. 2014; da Silva et al. 2014b; Colace et al. 2015; Liang et al. 2014; Cruz et al. 2014), sentence-level (Balahur and Perea-Ortega 2015; Yan et al. 2014; Zhang…”
BackgroundMethodInfluentialBERT: a sentiment analysis odyssey
Alaparthi, S., & Mishra, M. (2021) · Journal of Marketing Analytics · doi:10.1057/s41270-021-00109-8 · Cited by 313
A polarity analysis framework for Twitter messages
Lima, A. C. E. S., de Castro, L. N., & Corchado, J. M. (2015) · Applied Mathematics and Computation · doi:10.1016/j.amc.2015.08.059 · Cited by 144
Business Analytics in the Context of Big Data: A Roadmap for Research
Phillips-Wren, G. E., Iyer, L., Kulkarni, U. R., & Ariyachandra, T. R. (2015) · Communications of the Association for Information Systems · doi:10.17705/1cais.03723 · Cited by 130
“The significant growth in use of social media data has spurred research in analytic methods that can help firms monitor public sentiment of their brand and products (da Silva et al. 2014).”
Consensus vote models for detecting and filtering neutrality in sentiment analysis
Valdivia, A., Luzón, M. V., Cambria, E., & Herrera, F. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.03.007 · Cited by 130
“We fi nally compare and analyze the results and conclude that the polarity consensus voting models, together with neutrality fi ltering, outperform SAC results.”
BackgroundInfluentialEmotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)
Yousaf, A., Umer, M., Sadiq, S., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2020) · IEEE Access · doi:10.1109/access.2020.3047831 · Cited by 126
“In the past many studies have been performed on Twitter sentiment classification [1].”
BackgroundOver a decade of social opinion mining: a systematic review
Cortis, K., & Davis, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10030-2 · Cited by 151
ASA: A framework for Arabic sentiment analysis
Oussous, A., Benjelloun, F.-Z., Lahcen, A. A., & Belfkih, S. (2019) · Journal of Information Science · doi:10.1177/0165551519849516 · Cited by 122
Sentiment analysis techniques in recent works
Madhoushi, Z., Hamdan, A. R., & Zainudin, S. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237157 · Cited by 107
A Novel Business Process Prediction Model Using a Deep Learning Method
Mehdiyev, N., Evermann, J., & Fettke, P. (2018) · Business & Information Systems Engineering · doi:10.1007/s12599-018-0551-3 · Cited by 104
“This leads to imbalanced event logs, where some events are highly prevalent and others are only sparsely represented, which are a challenge for classifier training.”
BackgroundKnowledge discovery out of text data: a systematic review via text mining
Usai, A., Pironti, M., Mital, M., & Mejri, C. A. (2018) · Journal of Knowledge Management · doi:10.1108/jkm-11-2017-0517 · Cited by 103
Financial Sentiment Lexicon Analysis
Sohangir, S., Petty, N., & Wang, D. (2018) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2018.00052 · Cited by 101
“Supervised classification methods, such as Support Vector Machines, Naı̈ve Bayes or ensembles [4], [5] have been deployed to perform sentiment analysis in multiple research projects.”
MethodALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs
Keshavarz, H., & Abadeh, M. S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.01.028 · Cited by 99
Sarcasm identification in textual data: systematic review, research challenges and open directions
Eke, C. I., Norman, A. A., Shuib, L. M., & Nweke, H. F. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09791-8 · Cited by 99
“The content-based feature extraction technique consists of Bag of the word (BoW) (da Silva et al. 2014), word to vector (word2vec) (Lee et al. 2018) and n-gram (Sintsova and Pu 2016) technique.”
MethodA Survey of Cutting-edge Multimodal Sentiment Analysis
Singh, U., Abhishek, K., & Azad, H. K. (2024) · ACM Computing Surveys · doi:10.1145/3652149 · Cited by 103
“Several works have been found to identify a user’s sentiment at the document-level [22, 42, 155, 211, 228].”
BackgroundSentiment analysis for online reviews using conditional random fields and support vector machines
Xia, H., Yang, Y., Pan, X., Zhang, Z., & An, W. (2019) · Electronic Commerce Research · doi:10.1007/s10660-019-09354-7 · Cited by 92
“Syntactic dependency analysis involves using the sequence of words in the sentence and word POS features to analyze the dependencies between words and uncover different semantic structures, such as the verb–object and verb–complement relations, based on certain rules [25–27].”
MethodUmer, M., Ashraf, I., Mehmood, A., Kumari, S., Ullah, S., & Choi, G. S. (2020) · Computational Intelligence · doi:10.1111/coin.12415 · Cited by 102
Nagarajan, S. M., & Gandhi, U. D. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3476-3 · Cited by 90
“Da Silve et al. [15] proposed a method for Twitter sentiment classification using a classifier.”
MethodRetractedA Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning
da Silva, N. F. F., Coletta, L. F. S., & Hruschka, E. R. (2016) · ACM Computing Surveys · doi:10.1145/2932708 · Cited by 88
“More recently, classifier ensembles have been successfully used [da Silva et al. 2014; Silva et al. 2014; Lin and Kolcz 2012; Clark and Wicentwoski 2013; Rodrıguez-Penagos et al. 2013; Hassan et al. 2013].”
MethodSentiment analysis in MOOCs: A case study
Moreno-Marcos, P. M., Alario‐Hoyos, C., Muñoz‐Merino, P. J., Ayres, I. E., & Kloos, C. D. (2018) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2018.8363409 · Cited by 87
“These variables have been computed at a message level and then they have been fed to different algorithms [37][38]: • Logistic Regression .”
MethodOn the evaluation and combination of state-of-the-art features in Twitter sentiment analysis
Carvalho, J., & Plastino, A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09895-6 · Cited by 87
A computational model for mining consumer perceptions in social media
Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.09.018 · Cited by 84
An abusive text detection system based on enhanced abusive and non-abusive word lists
Lee, H.-S., Lee, H.-R., Park, J.-U., & Han, Y.-S. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.06.009 · Cited by 84
Sentiment Analysis in Social Networks
Fersini, E. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804412-4.00006-1 · Cited by 83
Urdu Sentiment Analysis Using Supervised Machine Learning Approach
Mukhtar, N., & Khan, M. A. (2017) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001418510011 · Cited by 80
Oliveira, D. J. S., Bermejo, P. H. D. S., & dos Santos, P. A. (2016) · Journal of Information Technology & Politics · doi:10.1080/19331681.2016.1214094 · Cited by 79
“According to Silva et al. (2014), Twitter also can be used to identify the acceptance or rejection of a political candidate by society.”
BackgroundInfluentialSurvey of Sentiment Analysis Using Deep Learning Techniques
Prabha, M., & Srikanth, G. U. (2019) · International Conference on Innovations in Information and Communication Technology (ICIICT) · doi:10.1109/iciict1.2019.8741438 · Cited by 81
“Sentence modelling uses bag-ofwords (BoW) [27] approach.”
An efficient approach for sentiment analysis using machine learning algorithm
Naresh, A., & Krishna, P. V. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00429-1 · Cited by 77
Sentiment classification of online consumer reviews using word vector representations
Bansal, B., & Srivastava, S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.029 · Cited by 76
Paolanti, M., Mancini, A., Frontoni, E., Felicetti, A., Marinelli, L., Marcheggiani, E., & Pierdicca, R. (2021) · Information Technology & Tourism · doi:10.1007/s40558-021-00196-4 · Cited by 87
Technology in the 21st century: New challenges and opportunities
Sheng, J., Amankwah‐Amoah, J., & Wang, X. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.06.009 · Cited by 73
“Da Silva et al. (2014) Microblogging Classifier ensembles can improve classification accuracy of microblogging sentiment analysis.”
Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets
Abdar, M., Basiri, M. E., Yin, J., Habibnezhad, M., Chi, G., Nemati, S., & Asadi, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109781 · Cited by 71
Term weighting scheme for short-text classification: Twitter corpuses
Alsmadi, I. M., & Hoon, G. K. (2018) · Neural Computing and Applications · doi:10.1007/s00521-017-3298-8 · Cited by 70
“Many researchers focused on developing solutions that can enhance classification performance by improving selection and extraction methods [23] or learning approaches [24].”
BackgroundSentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach
Paredes‐Valverde, M. A., Colomo‐Palacios, R., Salas‐Zárate, M. D. P., & Valencia‐García, R. (2017) · Scientific Programming · doi:10.1155/2017/1329281 · Cited by 68
“Among the most used methods are bag of words [13], TFIDF [14], n-grams (unigrams, bigrams, and trigrams) [11, 15], features based on POS tagging [16], and features based on dependency rules [17].”
MethodUsing unsupervised information to improve semi-supervised tweet sentiment classification
da Silva, N. F. F., Coletta, L. F. S., Hruschka, E. R., & Junior, E. R. H. (2016) · Information Sciences · doi:10.1016/j.ins.2016.02.002 · Cited by 67
Sentiment analysis: Approaches and open issues
Shahnawaz, & Astya, P. (2017) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2017.8229791 · Cited by 64
“1) The principal issues in all methods for sentiment analysis are comparative andobjective sentences, classification accuracy and sarcasm because of these issues most of the sentiment analysis models classify most of the pieces of text from the opinionated data into inaccurate classes[7, 19-22],from which a major port…”
BackgroundA Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique
Gaye, B., Zhang, D., & Wulamu, A. (2021) · Information · doi:10.3390/info12090374 · Cited by 64
“By using decision trees with low error rates, this intern lowers the chances of wrong prediction [45].”
MethodAn enhanced approach for sentiment analysis based on meta-ensemble deep learning
Kora, R., & Mohammed, A. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01043-6 · Cited by 63
“In addition, the authors in Da Silva et al. (2014); Xia et al. (2016); Fersini et al. (2016); Araque et al. (2017); Saleena (2018) applied majority voting based on several traditional classifiers such as SVM, RF, LR, NB, DT, and ME for English sentiment analysis.”
A Multi-stage Deep Learning Approach for Business Process Event Prediction
Mehdiyev, N., Evermann, J., & Fettke, P. (2017) · IEEE Conference on Business Informatics · doi:10.1109/cbi.2017.46 · Cited by 63
“Feature hashing has already found successful applications in diverse domains such news categorization, spam filtering, sentiment analysis in social networks and different areas of bioinformatics [20], [25], [26], [27].”
BackgroundExploiting New Sentiment-Based Meta-level Features for Effective Sentiment Analysis
Canuto, S., Gonçalves, M. A., & Benevenuto, F. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835821 · Cited by 61
“The most recent effort regarding sentiment analysis towards this kind of meta-feature uses only the scores of the first level classifier’s output [18, 8].”
BackgroundGhosh, M., & Sanyal, G. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0152-5 · Cited by 60
“The author [41] proposed to use classifier ensembles and lexicons for sentiment analysis of tweets automatically.”
MethodMulticlass sentiment analysis on COVID-19-related tweets using deep learning models
Vernikou, S., Lyras, A., & Kanavos, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07650-2 · Cited by 62
“In many research papers [8, 21, 22], the removal of these words is followed, but in this case their replacement was chosen due to the fact that deep learning techniques are considered to respond better to as much information as possible, so that the meaning of the sentence is not lost when dealing…”
BackgroundSentiments analysis of Twitter data using data mining
Jain, A. P., & Katkar, V. D. (2015) · doi:10.1109/infop.2015.7489492 · Cited by 58
A two-phase sentiment analysis approach for judgement prediction
Liu, Y.-H., & Chen, Y. (2017) · Journal of Information Science · doi:10.1177/0165551517722741 · Cited by 57
Feature engineering for sentiment analysis in e-health forums
Carrillo-de-Albornoz, J., Rodríguez-Vidal, J., & Plaza, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207996 · Cited by 60
Analysis of Various Sentiment Classification Techniques
Vimalkumar, B., & Bhumika, M. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016909259 · Cited by 56
“Multinomial Naive Bayes, Logistic Regression, Random Forests and Support vector machine (LIBSVM) algorithms are ensemble to improve the performance [15].”
BackgroundMethodInfluentialThe added value of auxiliary data in sentiment analysis of Facebook posts
Meire, M., Ballings, M., & den Poel, D. V. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.06.013 · Cited by 59
Sentiment Analysis of News Headlines Based on Sentiment Lexicon and Deep Learning
Kaliappan, S., Natrayan, L., & Rajput, A. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10276102 · Cited by 55
“Sentiment analysis is widely used to find the thoughts and mentality of people with their tweets on Twitter using the classifier algorithm is proposed [6].”
MethodLeveraging semantics for sentiment polarity detection in social media
Dridi, A., & Recupero, D. R. (2017) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0727-z · Cited by 55
“Several approaches have been proposed to solve polarity detection problem within microblogs ranging from supervised approaches [1, 3, 6, 10, 11, 18, 21], unsupervised approaches [15, 16, 25, 28, 31] to hybrid ones [13, 14].”
BackgroundMethodUsing Support Vector Machine Ensembles for Target Audience Classification on Twitter
Lo, S. L., Chiong, R., & Cornforth, D. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122855 · Cited by 54
“Previous work using SVM ensembles or combination of an SVM with other types of classifiers in understanding Twitter data can be found in the area of sentiment analysis, in which a bootstrap ensemble framework was proposed to handle both the sentiment multi-class imbalance and data sparsity issues [32] as well as senti…”
MethodAuthorship verification applied to detection of compromised accounts on online social networks
Barbon, S., Igawa, R. A., & Zarpel�ão, B. B. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3899-8 · Cited by 53
“Currently, OSN are used as a resource of information and research in several areas, which include Sentiment Analysis (SA) [15, 23, 29] and Recommender Systems (RS) [46, 47, 62].”
BackgroundHybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter
Awwalu, J., Bakar, A. A., & Yaakub, M. R. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04248-z · Cited by 53
“Similarly, [23] used classifier ensemble and lexicon approach to automatically classify sentiments from tweets sourced from several datasets.”
A practical guide to text mining with topic extraction
Karl, A. T., Wisnowski, J. W., & Rushing, W. H. (2015) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1361 · Cited by 56
A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis
Bibi, M., Aziz, W., Almaraashi, M., Khan, I. H., Nadeem, M. S. A., & Habib, N. (2020) · IEEE Access · doi:10.1109/access.2020.2983859 · Cited by 55
“In another work [28], a cooperative framework of Naïve Bayes, random forest, SVM and logistics regression is designed for classification of positive and negative tweets.”
MethodSentiment analysis: An automatic contextual analysis and ensemble clustering approach and comparison
AL-Sharuee, M. T., Liu, F., & Pratama, M. (2018) · Data & Knowledge Engineering · doi:10.1016/j.datak.2018.04.001 · Cited by 55
Automated Classification of Societal Sentiments on Twitter With Machine Learning
Vyas, P., Martin Reisslein, B., Rimal, B. P., Vyas, G., Basyal, G. P., & Muzumdar, P. (2021) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2021.3108963 · Cited by 56
Identifying Mubasher software products through sentiment analysis of Arabic tweets
AL-Rubaiee, H., Qiu, R., & Li, D. (2016) · International Conference on Industrial Informatics and Computer Systems (CIICS) · doi:10.1109/iccsii.2016.7462396 · Cited by 50
“Duncan and Zhang [18] claimed that Twitter sentiment analysis is different from traditional sentiment analysis Which should be addressed.”
BackgroundAutomatic Classification of Semantic Content of Classroom Dialogue
Song, Y., Lei, S., Hao, T., Lan, Z., & Ding, Y. (2020) · Journal of Educational Computing Research · doi:10.1177/0735633120968554 · Cited by 54
“For example, Silva et al. (2014) proposed a technique that automated the detection of Tweets sentiment using classifier ensembles consisting of random forest, logistic regression, support vector machine and multinomial naive Bayes.”
MethodEntropy based classifier for cross-domain opinion mining
Deshmukh, J. S., & Tripathy, A. K. (2017) · Applied Computing and Informatics · doi:10.1016/j.aci.2017.03.001 · Cited by 49
Efficient Twitter Sentiment Analysis System with Feature Selection and Classifier Ensemble
Fouad, M. M., Gharib, T. F., & Mashat, A. S. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-74690-6_51 · Cited by 49
Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data
Sharef, N. M., Zin, H. M., & Nadali, S. (2016) · Journal of Computer Science · doi:10.3844/jcssp.2016.153.168 · Cited by 52
“A number of ML techniques have been adopted to perform the classification task in SA (da Silva et al ., 2014; Go et al ., 2009; Xia et al ., 2011).”
MethodA Deep Neural Architecture for Sentence-Level Sentiment Classification in Twitter Social Networking
Nguyen, H., & Nguyen, L.-M. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8438-6_2 · Cited by 48
“For Sander dataset, we use standard 10-fold cross validation as [15].”
BackgroundMethodInfluentialReview of short-text classification
Alsmadi, I. M., & Gan, K. H. (2019) · International Journal of Web Information Systems · doi:10.1108/ijwis-12-2017-0083 · Cited by 48
Hybrid Words Representation for Airlines Sentiment Analysis
Naseem, U., Khan, S. K., Razzak, I., & Hameed, I. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35288-2_31 · Cited by 47
Sentiment Classification: Feature Selection Based Approaches Versus Deep Learning
Uysal, A. K., & Murphey, Y. L. (2017) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2017.53 · Cited by 46
“analyzed the performance of classifier ensembles for Twitter sentiment classification [6].”
BackgroundWeng, B., Martinez, W. G., Tsai, Y., Li, C., Lu, L., Barth, J. R., & Megahed, F. M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.07.024 · Cited by 48
Kaur, G., Kaushik, A., & Sharma, S. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3030037 · Cited by 46
“Da Silva et al. [15] used the bag of words and feature hashing for extracting the features.”
BackgroundPerformance analysis of sentiments in Twitter dataset using SVM models
Ramasamy, L. K., Kadry, S., Nam, Y., & Meqdad, M. N. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i3.pp2275-2284 · Cited by 46
Islam, A., Uddin, M. A., Aryal, S., & Stea, G. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103618 · Cited by 45
A Content and Sentiment Analysis of Greek Tweets during the Pandemic
Kydros, D. A., Argyropoulou, M., & Vrana, V. G. (2021) · Sustainability · doi:10.3390/su13116150 · Cited by 44
CitEnergy : A BERT based model to analyse Citizens’ Energy-Tweets
Bedi, J., & Toshniwal, D. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.103706 · Cited by 43
Aspect based sentiment analysis in social media with classifier ensembles
Perikos, I., & Hatzilygeroudis, I. (2017) · International Conference on Interaction Sciences · doi:10.1109/icis.2017.7960005 · Cited by 42
“In the work presented in [4], authors explore tweet sentiment analysis using classifier ensembles.”
BackgroundThe association between stock price volatility and financial news – a sentiment analysis approach
Seng, J., & Yang, H.-F. (2017) · Kybernetes · doi:10.1108/k-11-2016-0307 · Cited by 42
Multi-view ensemble learning method for microblog sentiment classification
Ye, X., Dai, H., Dong, L.-A., & Wang, X. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113987 · Cited by 41
Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media
Chen, H., McKeever, S., & Delany, S. J. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-46562-3_12 · Cited by 39
“[7] presented an ensemble classifier that predicts content by averaging a variety of classifiers results in order to avoid over-fitting.”
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks
Attia, M. A., Samih, Y., Elkahky, A. M., & Kallmeyer, L. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4suqsh39s5kg · Cited by 39
“Da Silva et al. (2014) used the Ensemble method (training multiple learners to solve the same problem) and reported an accuracy score of 76.”
MethodImproving Sentiment Analysis of Moroccan Tweets Using Ensemble Learning
Oussous, A., Lahcen, A. A., & Belfkih, S. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-96292-4_8 · Cited by 39
E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation
López, M., Valdivia, A., Martínez‐Cámara, E., Luzón, M. V., & Herrera, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.038 · Cited by 39
Evaluation of ensemble-based sentiment classifiers for Twitter data
Troussas, C., Krouska, A., & Virvou, M. K. (2016) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2016.7785380 · Cited by 38
“Table I shows the ensemble approaches used.”
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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