{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T04:28:30Z","timestamp":1728016110552},"reference-count":104,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1016\/j.eswa.2022.118817","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T16:33:22Z","timestamp":1663173202000},"page":"118817","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["A novel flexible feature extraction algorithm for Spanish tweet sentiment analysis based on the context of words"],"prefix":"10.1016","volume":"212","author":[{"given":"Garc\u00eda-D\u00edaz","family":"Pilar","sequence":"first","affiliation":[]},{"given":"S\u00e1nchez-Berriel","family":"Isabel","sequence":"additional","affiliation":[]},{"given":"Pontiel-Mart\u00edn","family":"Diego","sequence":"additional","affiliation":[]},{"given":"Gonz\u00e1lez-\u00c1vila","family":"Jos\u00e9 Luis","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2022.118817_b0005","series-title":"International conference on intelligent text processing and computational linguistics","first-page":"13","article-title":"Optimal feature selection for sentiment analysis","author":"Agarwal","year":"2013"},{"key":"10.1016\/j.eswa.2022.118817_b0010","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.procs.2019.05.008","article-title":"The impact of features extraction on the sentiment analysis","volume":"152","author":"Ahuja","year":"2019","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.eswa.2022.118817_b0015","unstructured":"Ahuja, R., Rastogi, H., Choudhuri, A., & Garg, B. (2015, March). Stock market forecast using sentiment analysis. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (pp 1008-1010). IEEE."},{"key":"10.1016\/j.eswa.2022.118817_b0020","series-title":"Sentiment Analysis on Different Domains Using Machine Learning Algorithms. In Advances in Data and Information Sciences","first-page":"143","author":"Ahuja","year":"2022"},{"key":"10.1016\/j.eswa.2022.118817_b0025","first-page":"1","article-title":"Social media use in 2021","volume":"1","author":"Auxier","year":"2021","journal-title":"Pew Research Center"},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0030","doi-asserted-by":"crossref","first-page":"225","DOI":"10.5455\/msm.2021.33.225-230","article-title":"Advances of Sentiment Analysis Applications in Obstetrics\/Gynecology and Midwifery","volume":"33","author":"Barbounaki","year":"2021","journal-title":"Materia Socio-Medica"},{"key":"10.1016\/j.eswa.2022.118817_b0035","first-page":"77","article-title":"Sentiment analysis and topic classification based on binary maximum entropy classifiers","volume":"50","author":"Batista","year":"2013","journal-title":"Procesamiento del lenguaje natural"},{"key":"10.1016\/j.eswa.2022.118817_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107134","article-title":"A comprehensive survey on sentiment analysis: Approaches, challenges and trends","volume":"226","author":"Birjali","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.118817_b0045","unstructured":"Cambria, E., Liu, Q., Decherchi, S., Xing, F., & Kwok, K. (2022). SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. Proceedings of LREC 2022."},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0050","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1007\/s10462-020-09895-6","article-title":"On the evaluation and combination of state-of-the-art features in twitter sentiment analysis","volume":"54","author":"Carvalho","year":"2021","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2022.118817_b0055","first-page":"1","article-title":"State of the art: A review of sentiment analysis based on sequential transfer learning","author":"Chan","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2022.118817_b0060","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2022.03.027","article-title":"Aspect-based sentiment analysis with component focusing multi-head co-attention networks","volume":"489","author":"Cheng","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118817_b0065","doi-asserted-by":"crossref","unstructured":"Choi, Y., & Cardie, C. (2009). Adapting a polarity lexicon using integer linear programming for domain specific sentiment classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2-Volume 2, pages 590\u2013598. Association for Computational Linguistics.","DOI":"10.3115\/1699571.1699590"},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0070","doi-asserted-by":"crossref","first-page":"178","DOI":"10.18201\/ijisae.2018644774","article-title":"An empirical study of the extreme learning machine for Twitter sentiment analysis","volume":"6","author":"Coban","year":"2018","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"key":"10.1016\/j.eswa.2022.118817_b0075","first-page":"15","article-title":"An\u00e1lisis de Sentimiento en el dominio salud: Analizando comentarios sobre f\u00e1rmacos","volume":"63","author":"Col\u00f3n-Ruiz","year":"2019","journal-title":"Procesamiento del Lenguaje Natural"},{"issue":"18","key":"10.1016\/j.eswa.2022.118817_b0080","doi-asserted-by":"crossref","first-page":"13597","DOI":"10.1007\/s00500-019-04368-7","article-title":"Sentiment analysis of expectation and perception of MILANO EXPO2015 in twitter data: A generalized cross entropy approach","volume":"24","author":"Corallo","year":"2020","journal-title":"Soft Computing"},{"key":"10.1016\/j.eswa.2022.118817_b0085","first-page":"22","article-title":"Big data as a source of statistical information","volume":"69","author":"Daas","year":"2014","journal-title":"The Survey Statistician"},{"key":"10.1016\/j.eswa.2022.118817_b0090","unstructured":"Daas, P., & Puts, M. (2014b). Social media sentiment and consumer confidence. European Central Bank Statistics paper series No. 5, Frankfurt Germany."},{"key":"10.1016\/j.eswa.2022.118817_b0095","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.asoc.2018.01.040","article-title":"A group incremental feature selection for classification using rough set theory based genetic algorithm","volume":"65","author":"Das","year":"2018","journal-title":"Applied Soft Computing"},{"issue":"10","key":"10.1016\/j.eswa.2022.118817_b0100","doi-asserted-by":"crossref","first-page":"15391","DOI":"10.1007\/s11042-020-10323-8","article-title":"Joint evaluation of preprocessing tasks with classifiers for sentiment analysis in Brazilian Portuguese language","volume":"80","author":"de Oliveira","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.eswa.2022.118817_b0105","doi-asserted-by":"crossref","unstructured":"Devi, W. R., & Chingangbam, C. (2021). Sentiment Analysis for Electoral Prediction Using Twitter Data. In Emerging Technologies in Data Mining and Information Security (pp. 25-33). Springer, Singapore.","DOI":"10.1007\/978-981-33-4367-2_3"},{"key":"10.1016\/j.eswa.2022.118817_b0110","unstructured":"Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"10.1016\/j.eswa.2022.118817_b0115","first-page":"77","article-title":"TASS 2018: The strength of deep learning in language understanding tasks","volume":"62","author":"D\u00edaz Galiano","year":"2019","journal-title":"Procesamiento del Lenguaje Natural"},{"key":"10.1016\/j.eswa.2022.118817_b0120","unstructured":"D\u00edaz-Galiano, M. C., Vega, M. G., Casasola, E., Chiruzzo, L., Cumbreras, M. \u00c1. G., C\u00e1mara, E. M., ... & Miranda-Jim\u00e9nez, S. (2019b). Overview of TASS 2019: One More Further for the Global Spanish Sentiment Analysis Corpus. In IberLEF@ SEPLN (pp. 550-560)."},{"key":"10.1016\/j.eswa.2022.118817_b0125","unstructured":"D\u00edaz Galiano, M. C., Mart\u00ednez C\u00e1mara, E., Garc\u00eda Cumbreras, M. \u00c1., Garc\u00eda Vega, M., & Villena Rom\u00e1n, J. (2018). The democratization of deep learning in TASS 2017."},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0130","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40649-020-00080-x","article-title":"A review: Preprocessing techniques and data augmentation for sentiment analysis","volume":"8","author":"Duong","year":"2021","journal-title":"Computational Social Networks"},{"key":"10.1016\/j.eswa.2022.118817_b0135","doi-asserted-by":"crossref","unstructured":"El Rahman, Sahar A., Alotaibi, F. A., & Alshehri, W. A. (2019). Sentiment analysis of twitter data. In 2019 international conference on computer and information sciences (ICCIS). IEEE, pp 1-4.","DOI":"10.1109\/ICCISci.2019.8716464"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0140","article-title":"Arabic text classification using maximum entropy","volume":"15","author":"El-Halees","year":"2015","journal-title":"IUG Journal of Natural Studies"},{"issue":"1\u20132","key":"10.1016\/j.eswa.2022.118817_b0145","first-page":"79","article-title":"The grouping genetic algorithms: Widening the scope of the GA's","volume":"33","author":"Falkenauer","year":"1993","journal-title":"JORBEL-Belgian Journal of Operations Research, Statistics, and Computer Science"},{"key":"10.1016\/j.eswa.2022.118817_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108509","article-title":"Genetic programming for feature extraction and construction in image classification","volume":"118","author":"Fan","year":"2022","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.118817_b0155","unstructured":"Fern\u00e1ndez V\u00edtores, D. (2020). El espa\u00f1ol: una lengua viva. Informe 2019. Instituto Cervantes. https:\/\/cvc.cervantes.es\/lengua\/espanol_lengua_viva\/pdf\/espanol_lengua_viva_2019.pdf."},{"key":"10.1016\/j.eswa.2022.118817_b0160","doi-asserted-by":"crossref","unstructured":"Forrest, S. (1996). Genetic algorithms. ACM Computing Surveys (CSUR), 28(1), 77-80.","DOI":"10.1145\/234313.234350"},{"key":"10.1016\/j.eswa.2022.118817_b0165","first-page":"33","article-title":"TASS 2015 - The evolution of the Spanish opinion mining systems","volume":"56","author":"Garc\u00eda-Cumbreras","year":"2016","journal-title":"Procesamiento de Lenguaje Natural"},{"key":"10.1016\/j.eswa.2022.118817_b0170","unstructured":"Garc\u00eda-Cumbreras, M. A., Villena-Rom\u00e1n, J., Mart\u00ednez-C\u00e1mara, E., D\u00edaz-Galiano, M. C., Mart\u00edn-Valdivia, M. T. & Ure\u00f1a L\u00f3pez, L. A. (2016b). Overview of tass 2016. In TASS 2016: Workshop on Sentiment Analysis at SEPLN, pp 13-21."},{"issue":"2","key":"10.1016\/j.eswa.2022.118817_b0175","doi-asserted-by":"crossref","first-page":"1916","DOI":"10.1016\/j.ygeno.2019.11.004","article-title":"Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data","volume":"112","author":"Garc\u00eda-D\u00edaz","year":"2020","journal-title":"Genomics"},{"issue":"9","key":"10.1016\/j.eswa.2022.118817_b0180","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.3390\/e22091020","article-title":"Evolutionary optimization of ensemble learning to determine sentiment polarity in an unbalanced multiclass corpus","volume":"22","author":"Garc\u00eda-Mendoza","year":"2020","journal-title":"Entropy"},{"key":"10.1016\/j.eswa.2022.118817_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113176","article-title":"A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification","volume":"146","author":"Gokalp","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2022.118817_b0190","doi-asserted-by":"crossref","unstructured":"Gondhi, N. K., Sharma, E., Alharbi, A. H., Verma, R., & Shah, M. A. (2022). Efficient Long Short-Term Memory-Based Sentiment Analysis of E-Commerce Reviews. Computational Intelligence and Neuroscience, 2022.","DOI":"10.1155\/2022\/3464524"},{"key":"10.1016\/j.eswa.2022.118817_b0195","doi-asserted-by":"crossref","unstructured":"Gu, Y. H., Yoo, S. J., Jiang, Z., Lee, Y. J., Piao, Z., Yin, H., & Jeon, S. (2018, January). Sentiment analysis and visualization of Chinese tourism blogs and reviews. In 2018 International Conference on Electronics, Information, and Communication (ICEIC), pp 1-4. IEEE.","DOI":"10.23919\/ELINFOCOM.2018.8330589"},{"key":"10.1016\/j.eswa.2022.118817_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105383","article-title":"Predicting literature\u2019s early impact with sentiment analysis in Twitter","volume":"192","author":"Hassan","year":"2020","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0210","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s10796-017-9820-9","article-title":"Sharing political content in online social media: A planned and unplanned behaviour approach","volume":"20","author":"Hossain","year":"2018","journal-title":"Information Systems Frontiers"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0215","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.1016\/j.eswa.2022.118817_b0220","series-title":"In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE","first-page":"629","article-title":"Twitter Sentiment Analysis using Machine Learning","author":"Ikram","year":"2022"},{"key":"10.1016\/j.eswa.2022.118817_b0225","unstructured":"Imran, M., Akhtar, A., Said, A., Safder, I., Hassan, S. U., & Aljohani, N. R. (2018, September). Exploiting social networks of Twitter in altmetrics big data. In STI 2018 Conference Proceedings (pp. 1339-1344). Centre for Science and Technology Studies (CWTS)."},{"key":"10.1016\/j.eswa.2022.118817_b0230","doi-asserted-by":"crossref","first-page":"14637","DOI":"10.1109\/ACCESS.2019.2892852","article-title":"A hybrid framework for sentiment analysis using genetic algorithm based feature reduction","volume":"7","author":"Iqbal","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2022.118817_b0235","doi-asserted-by":"crossref","unstructured":"Jagdale, J., Reha, A. Y., & Emmanuel, M. (2022). Sentimental Evaluation of Sensitive Tweets Using Hybrid Sentiment Analysis Model. In Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems (pp 889-897). Springer, Singapore.","DOI":"10.1007\/978-981-16-7330-6_65"},{"issue":"2","key":"10.1016\/j.eswa.2022.118817_b0240","doi-asserted-by":"crossref","first-page":"659","DOI":"10.3233\/JIFS-189738","article-title":"Sentiment classification using hybrid feature selection and ensemble classifier","volume":"42","author":"Jain","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"4","key":"10.1016\/j.eswa.2022.118817_b0245","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s40647-018-0226-y","article-title":"Social media techno-discursive design, affective communication and contemporary politics","volume":"11","author":"Khosravinik","year":"2018","journal-title":"Fudan Journal of the Humanities and Social Sciences"},{"key":"10.1016\/j.eswa.2022.118817_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijinfomgt.2020.102280","article-title":"The influence of informal social media practices on knowledge sharing and work processes within organizations","volume":"58","author":"Kwayu","year":"2021","journal-title":"International Journal of Information Management"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102784","article-title":"E-word of mouth sentiment analysis for user behavior studies","volume":"59","author":"Li","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2022.118817_b0260","doi-asserted-by":"crossref","DOI":"10.1109\/TAFFC.2021.3071388","article-title":"Embedding Refinement Framework for Targeted Aspect-based Sentiment Analysis","author":"Liang","year":"2021","journal-title":"IEEE Transactions on Affective Computing"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0265","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02145-9","article-title":"Sentiment analysis and opinion mining","volume":"5","author":"Liu","year":"2012","journal-title":"Synthesis lectures on human language technologies"},{"issue":"9","key":"10.1016\/j.eswa.2022.118817_b0270","doi-asserted-by":"crossref","first-page":"6313","DOI":"10.1007\/s11042-019-08409-z","article-title":"Efficient feature selection techniques for sentiment analysis","volume":"79","author":"Madasu","year":"2020","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"10.1016\/j.eswa.2022.118817_b0275","first-page":"34","article-title":"An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis","volume":"6","author":"Madhu","year":"2018","journal-title":"International Journal of Scientific Research in Computer Science"},{"key":"10.1016\/j.eswa.2022.118817_b0280","doi-asserted-by":"crossref","unstructured":"Mhamed, M., Sutcliffe, R., Sun, X., Feng, J., Almekhlafi, E., & Retta, E. A. (2021). Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing. Computational Intelligence and Neuroscience, 2021.","DOI":"10.1155\/2021\/5538791"},{"key":"10.1016\/j.eswa.2022.118817_b0285","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26."},{"key":"10.1016\/j.eswa.2022.118817_b0290","unstructured":"A. Mudinas D. Zhang M. Levene Market trend prediction using sentiment analysis: Lessons learned and paths forward 2019 arXiv preprint arXiv:1903.05440."},{"issue":"5","key":"10.1016\/j.eswa.2022.118817_b0295","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1002\/cb.1925","article-title":"Customers' social interactions and panic buying behavior: Insights from social media practices","volume":"20","author":"Naeem","year":"2021","journal-title":"Journal of Consumer Behaviour"},{"key":"10.1016\/j.eswa.2022.118817_b0300","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.future.2020.06.050","article-title":"Transformer based deep intelligent contextual embedding for twitter sentiment analysis","volume":"113","author":"Naseem","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"10.1016\/j.eswa.2022.118817_b0305","doi-asserted-by":"crossref","unstructured":"Ni, R., & Cao, H. (2020). Sentiment Analysis based on GloVe and LSTM-GRU. In 2020 39th Chinese Control Conference (CCC) pp 7492-7497. IEEE.","DOI":"10.23919\/CCC50068.2020.9188578"},{"key":"10.1016\/j.eswa.2022.118817_b0310","unstructured":"Nigam, K., Lafferty, J., & McCallum, A. (1999, August). Using maximum entropy for text classification. In IJCAI-99 workshop on machine learning for information filtering Vol. 1(1), pp 61-67."},{"key":"10.1016\/j.eswa.2022.118817_b0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.jretconser.2021.102630","article-title":"Big social data and customer decision making in vegetarian restaurants: A combined machine learning method","volume":"62","author":"Nilashi","year":"2021","journal-title":"Journal of Retailing and Consumer Services"},{"issue":"7","key":"10.1016\/j.eswa.2022.118817_b0320","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.3390\/s21072266","article-title":"Building a twitter sentiment analysis system with recurrent neural networks","volume":"21","author":"Nistor","year":"2021","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.eswa.2022.118817_b0325","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1016\/j.jksuci.2022.02.025","article-title":"Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification","volume":"34","author":"Onan","year":"2022","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0330","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1177\/0165551515613226","article-title":"A feature selection model based on genetic rank aggregation for text sentiment classification","volume":"43","author":"Onan","year":"2017","journal-title":"Journal of Information Science"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0335","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1093\/comjnl\/bxz163","article-title":"Sentiment classification using two effective optimization methods derived from the artificial bee colony optimization and imperialist competitive algorithm","volume":"65","author":"Osmani","year":"2022","journal-title":"The Computer Journal"},{"key":"10.1016\/j.eswa.2022.118817_b0340","first-page":"805","article-title":"Twitter Sentiment Analysis of the 2019 Indian Election. In IOT with Smart Systems","volume":"2022","author":"Passi","year":"2022","journal-title":"Springer, Singapore"},{"key":"10.1016\/j.eswa.2022.118817_b0345","series-title":"In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)","first-page":"1532","article-title":"October). Glove: Global vectors for word representation","author":"Pennington","year":"2014"},{"key":"10.1016\/j.eswa.2022.118817_b0350","doi-asserted-by":"crossref","unstructured":"Pintas, J. T., Fernades, L. A. F.; Garcia, A. C. B. (2021). Feature selection methods for text classification: a systematic literature review. Artificial Intelligence Review, 2021, vol. 54(8), pp 6149-6200.","DOI":"10.1007\/s10462-021-09970-6"},{"year":"2020","series-title":"Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research","author":"Poria","key":"10.1016\/j.eswa.2022.118817_b0355"},{"key":"10.1016\/j.eswa.2022.118817_b0360","doi-asserted-by":"crossref","unstructured":"Rathika, J., & Soranamageswari, M. (2022). Intensified Gray Wolf Optimization-based Extreme Learning Machine for Sentiment Analysis in Big Data. In Evolution in Signal Processing and Telecommunication Networks (pp 103-114). Springer, Singapore.","DOI":"10.1007\/978-981-16-8554-5_11"},{"key":"10.1016\/j.eswa.2022.118817_b0365","series-title":"In 2016 IEEE International Conference on Big Data Analysis (ICBDA)","first-page":"1","article-title":"Unsupervised feature selection for text classification via word embedding","author":"Rui","year":"2016"},{"key":"10.1016\/j.eswa.2022.118817_b0370","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/978-3-030-91103-4_5","article-title":"A Comprehensive Review on Brain Disease Mapping\u2014The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals","author":"Sachadev","year":"2022","journal-title":"Medical Informatics and Bioimaging Using Artificial Intelligence"},{"key":"10.1016\/j.eswa.2022.118817_b0375","first-page":"8887","article-title":"Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis","volume":"975","author":"Salam","year":"2020","journal-title":"International Journal of Computer Applications"},{"key":"10.1016\/j.eswa.2022.118817_b0380","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jbi.2015.02.004","article-title":"Utilizing social media data for pharmacovigilance: A review","volume":"54","author":"Sarker","year":"2015","journal-title":"Journal of biomedical informatics"},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0385","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3390\/computers8030055","article-title":"Semantic features for optimizing supervised approach of sentiment analysis on product reviews","volume":"8","author":"Setya Rintyarna","year":"2019","journal-title":"Computers"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0390","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s12652-018-0862-8","article-title":"Sentiment analysis: A review and comparative analysis over social media","volume":"11","author":"Singh","year":"2020","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"10.1016\/j.eswa.2022.118817_b0395","first-page":"1","article-title":"Genetic algorithms in the fields of artificial intelligence and data sciences","author":"Sohail","year":"2021","journal-title":"Annals of Data Science"},{"key":"10.1016\/j.eswa.2022.118817_b0400","first-page":"163","article-title":"A Review on Multipolarity in Sentiment Analysis. In Information and Communication Technology for Competitive Strategies (ICTCS 2020)","volume":"2022","author":"Srivastava","year":"2022","journal-title":"Springer, Singapore"},{"key":"10.1016\/j.eswa.2022.118817_b0405","series-title":"Measuring the Impact of Online Media on Consumers, Businesses and Society. Sustainable Management, Wertsch\u00f6pfung und Effizienz","article-title":"Sentiment Analysis as a New Source of Information","author":"Starosta","year":"2022"},{"key":"10.1016\/j.eswa.2022.118817_b0410","doi-asserted-by":"crossref","first-page":"21517","DOI":"10.1109\/ACCESS.2022.3152828","article-title":"RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network","volume":"10","author":"Tan","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2022.118817_b0415","series-title":"October). Sentence-level sentiment polarity classification using a linguistic approach","first-page":"77","author":"Tan","year":"2011"},{"key":"10.1016\/j.eswa.2022.118817_b0420","unstructured":"TASS-2017: Workshop on Semantic Analysis at SEPLN. Available: http:\/\/www.sepln.org\/workshops\/tass\/2017\/ (Last access in April 2022)."},{"key":"10.1016\/j.eswa.2022.118817_b0425","first-page":"1","article-title":"Deep Learning Approach for Aspect-Based Sentiment Classification: A Comparative Review","volume":"2022","author":"Trisna","year":"2022","journal-title":"Applied Artificial Intelligence"},{"key":"10.1016\/j.eswa.2022.118817_b0430","doi-asserted-by":"crossref","unstructured":"Trivedi, S. K., & Singh, A. (2021). Twitter sentiment analysis of app based online food delivery companies. Global Knowledge, Memory and Communication.","DOI":"10.1108\/GKMC-04-2020-0056"},{"issue":"2","key":"10.1016\/j.eswa.2022.118817_b0435","first-page":"183","article-title":"Social media as a data source for official statistics; the Dutch Consumer Confidence Index","volume":"43","author":"Van den Brakel","year":"2017","journal-title":"Survey Methodology"},{"key":"10.1016\/j.eswa.2022.118817_b0440","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.inffus.2018.03.007","article-title":"Consensus vote models for detecting and filtering neutrality in sentiment analysis","volume":"44","author":"Valdivia","year":"2018","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2022.118817_b0445","series-title":"In 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE","first-page":"1275","article-title":"Aspect-level sentiment analysis on e-commerce data","author":"Vanaja","year":"2018"},{"issue":"3","key":"10.1016\/j.eswa.2022.118817_b0450","doi-asserted-by":"crossref","first-page":"51","DOI":"10.36348\/sjbms.2021.v06i03.001","article-title":"Impact of Social Media on Consumer Buying Behavior","volume":"6","author":"Varghese","year":"2021","journal-title":"Saudi Journal of Business and Management Studies (SJBMS)"},{"key":"10.1016\/j.eswa.2022.118817_b0455","doi-asserted-by":"crossref","unstructured":"Vashishtha, S., & Susan, S. (2019). Fuzzy rule based unsupervised sentiment analysis from social media posts. Expert Systems with Applications, 2019, vol. 138, pp 112834.","DOI":"10.1016\/j.eswa.2019.112834"},{"issue":"5","key":"10.1016\/j.eswa.2022.118817_b0460","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3390\/info12050204","article-title":"Twitter sentiment analysis towards covid-19 vaccines in the Philippines using na\u00efve bayes","volume":"12","author":"Villavicencio","year":"2021","journal-title":"Information"},{"key":"10.1016\/j.eswa.2022.118817_b0465","unstructured":"Villena-Rom\u00e1n, J., Garc\u00eda-Morera, J., Garc\u00eda-Cumbreras, M. A., Mart\u00ednez-C\u00e1mara, E., Mart\u00edn-Valdivia, M. T., & Ure\u00f1a L\u00f3pez, L. A. (2015). Overview of TASS 2015. In TASS 2015: Workshop on Sentiment Analysis at SEPLN, pp 13-21."},{"key":"10.1016\/j.eswa.2022.118817_b0470","doi-asserted-by":"crossref","unstructured":"Wang, H., & Hong, M. (2019). Supervised Hebb rule based feature selection for text classification. Information Processing & Management, 2019, vol. 56(1) pp 167-191.","DOI":"10.1016\/j.ipm.2018.09.004"},{"issue":"04","key":"10.1016\/j.eswa.2022.118817_b0475","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1142\/S0218488520500294","article-title":"Multi-level fine-scaled sentiment sensing with ambivalence handling","volume":"28","author":"Wang","year":"2020","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"issue":"1","key":"10.1016\/j.eswa.2022.118817_b0480","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s12559-019-09669-5","article-title":"Optimal feature selection for learning-based algorithms for sentiment classification","volume":"12","author":"Wang","year":"2020","journal-title":"Cognitive Computation"},{"key":"10.1016\/j.eswa.2022.118817_b0485","series-title":"Springer handbook of science and technology indicators","first-page":"687","article-title":"Social media metrics for new research evaluation","author":"Wouters","year":"2019"},{"issue":"2","key":"10.1016\/j.eswa.2022.118817_b0490","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s00500-017-2904-0","article-title":"An improved algorithm for sentiment analysis based on maximum entropy","volume":"23","author":"Xie","year":"2019","journal-title":"Soft Computing"},{"key":"10.1016\/j.eswa.2022.118817_b0495","doi-asserted-by":"crossref","unstructured":"Xue, L., Wang, H., Wang, F., & Ma, H. (2021, February). Sentiment Analysis of Stock Market Investors and Its Correlation with Stock Price Using Maximum Entropy. In International Conference on Intelligence Science (pp 29-44). Springer, Cham.","DOI":"10.1007\/978-3-030-79474-3_3"},{"key":"10.1016\/j.eswa.2022.118817_b0500","article-title":"Aspect-based sentiment analysis with new target representation and dependency attention","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Affective Computing"},{"key":"10.1016\/j.eswa.2022.118817_b0505","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, J., & Liu, L. (2021, December). Modelling Context with Graph Convolutional Networks for Aspect-based Sentiment Analysis. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp 194-200). IEEE.","DOI":"10.1109\/ICDMW53433.2021.00031"},{"key":"10.1016\/j.eswa.2022.118817_b0510","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ins.2022.03.082","article-title":"Aggregated graph convolutional networks for aspect-based sentiment classification","volume":"600","author":"Zhao","year":"2022","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2022.118817_b0515","article-title":"Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis","author":"Zhao","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2022.118817_b0520","doi-asserted-by":"crossref","unstructured":"Zucco, C., Liang, H., Di Fatta, G., & Cannataro, M. (2018). Explainable sentiment analysis with applications in medicine. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1740-1747). IEEE.","DOI":"10.1109\/BIBM.2018.8621359"},{"key":"10.1016\/j.eswa.2022.118817_b0525","first-page":"1","article-title":"An efficient two-state GRU based on feature attention mechanism for sentiment analysis","author":"Zulqarnain","year":"2022","journal-title":"Multimedia Tools and Applications"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422018358?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422018358?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T21:38:39Z","timestamp":1727991519000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417422018358"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":104,"alternative-id":["S0957417422018358"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2022.118817","relation":{},"ISSN":["0957-4174"],"issn-type":[{"type":"print","value":"0957-4174"}],"subject":[],"published":{"date-parts":[[2023,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel flexible feature extraction algorithm for Spanish tweet sentiment analysis based on the context of words","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2022.118817","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"118817"}}