{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:37Z","timestamp":1760145697791,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T00:00:00Z","timestamp":1724371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Many text mining methods use statistical information as a text- and language-independent approach for sentiment analysis. However, text mining methods based on stochastic patterns and rules require many samples for training. On the other hand, deterministic and non-probabilistic methods are easier and faster to solve than other methods, but they are inefficient when dealing with Natural Language Processing (NLP) data. This research presents a novel hybrid solution based on two mathematical approaches combined with a heuristic approach to solve unbalanced pseudo-linear algebraic equation systems that can be used as a sentiment word scoring system. In its first step, the proposed solution uses two mathematical approaches to find two initial populations for a heuristic method. The heuristic solution solves a pseudo-linear NLP scoring scheme in a polarity detection method and determines the final scores. The proposed solution was validated using three scenarios on the SemEval-2013 competition, the ESWC dataset, and the Taboada dataset. The simulation results revealed that the proposed solution is comparable to the best state-of-the-art methods in polarity detection.<\/jats:p>","DOI":"10.3390\/info15090513","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:53:19Z","timestamp":1724417599000},"page":"513","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Hierarchical Mathematical Heuristic Solution of Sparse Algebraic Equations in Sentiment Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Maryam","family":"Jalali","sequence":"first","affiliation":[{"name":"Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8648-4514","authenticated-orcid":false,"given":"Morteza","family":"Zahedi","sequence":"additional","affiliation":[{"name":"Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0863-1977","authenticated-orcid":false,"given":"Abdorreza Alavi","family":"Gharahbagh","sequence":"additional","affiliation":[{"name":"Faculdade de Engenharia, Universidade do Porto (FEUP), Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0842-8250","authenticated-orcid":false,"given":"Vahid","family":"Hajihashemi","sequence":"additional","affiliation":[{"name":"Faculdade de Engenharia, Universidade do Porto (FEUP), Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1094-0114","authenticated-orcid":false,"given":"Jos\u00e9 J. M.","family":"Machado","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Departamento de Engenharia Mec\u00e2nica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel R. S.","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Departamento de Engenharia Mec\u00e2nica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4873","DOI":"10.1007\/s10462-021-10030-2","article-title":"Over a decade of social opinion mining: A systematic review","volume":"54","author":"Cortis","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"41101","DOI":"10.1109\/ACCESS.2019.2906754","article-title":"A survey on opinion mining: From stance to product aspect","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s13278-021-00855-8","article-title":"Opinion mining in online social media: A survey","volume":"12","author":"Messaoudi","year":"2022","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alnahas, D., A\u015f\u0131k, F., Kanturvardar, A., and \u00dclkg\u00fcn, A.M. (2022, January 15\u201316). Opinion Mining Using LSTM Networks Ensemble for Multi-class Sentiment Analysis in E-commerce. Proceedings of the 2022 3rd International Informatics and Software Engineering Conference (IISEC), Ankara, Turkey.","DOI":"10.1109\/IISEC56263.2022.9998264"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.future.2017.10.028","article-title":"Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena","volume":"96","author":"Chen","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s13278-021-00813-4","article-title":"A survey on the use of data and opinion mining in social media to political electoral outcomes prediction","volume":"11","author":"Santos","year":"2021","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hajihashemi, V., Ameri, M.M.A., Gharahbagh, A.A., and Bastanfard, A. (2020, January 18\u201320). A pattern recognition based Holographic Graph Neuron for Persian alphabet recognition. Proceedings of the 2020 International conference on machine vision and image processing (MVIP), Qom, Iran.","DOI":"10.1109\/MVIP49855.2020.9116913"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, Q. (2021, January 29\u201331). Hot Spot Mining and Analysis Model of Sports Microblog Culture Public Opinion Based on Big Data Environment. Proceedings of the 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS52425.2021.9524275"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Piedrahita-Vald\u00e9s, H., Piedrahita-Castillo, D., Bermejo-Higuera, J., Guillem-Saiz, P., Bermejo-Higuera, J.R., Guillem-Saiz, J., Sicilia-Montalvo, J.A., and Mach\u00edo-Regidor, F. (2021). Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019. Vaccines, 9.","DOI":"10.3390\/vaccines9010028"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rubtsova, Y. (2018). Reducing the deterioration of sentiment analysis results due to the time impact. Information, 9.","DOI":"10.3390\/info9080184"},{"key":"ref_11","first-page":"361","article-title":"A study on sentiment analysis techniques of Twitter data","volume":"10","author":"Alsaeedi","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mittal, A., and Patidar, S. (2019, January 27\u201329). Sentiment analysis on twitter data: A survey. Proceedings of the 7th International Conference on Computer and Communications Management, Bangkok, Thailand.","DOI":"10.1145\/3348445.3348466"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/j.neucom.2017.09.080","article-title":"Textual sentiment analysis via three different attention convolutional neural networks and cross-modality consistent regression","volume":"275","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_14","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., and Qin, B. (2014, January 22\u201327). Learning sentiment-specific word embedding for twitter sentiment classification. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-1146"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ipm.2015.04.003","article-title":"Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis","volume":"52","author":"Xia","year":"2016","journal-title":"Inf. Process. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.knosys.2018.11.018","article-title":"Semi-supervised dimensional sentiment analysis with variational autoencoder","volume":"165","author":"Wu","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Samb, S.M.K., Kand\u00e9, D., Camara, F., and Ndiaye, S. (2019, January 6\u20139). Improved bilingual sentiment analysis lexicon using word-level trigram. Proceedings of the 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC47050.2019.9064223"},{"key":"ref_19","first-page":"2551","article-title":"Influence of Syntactic, Semantic and Stylistic Features for Sentiment Identification of Messages Using Svm Classifier","volume":"8","author":"Raju","year":"2019","journal-title":"Int. J. Sci. Technol. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ito, T., Tsubouchi, K., Sakaji, H., Izumi, K., and Yamashita, T. (2019, January 8\u201311). Csnn: Contextual sentiment neural network. Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China.","DOI":"10.1109\/ICDM.2019.00135"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kand\u00e9, D., Camara, F., Ndiaye, S., and Guirassy, F.M. (2019, January 24\u201327). FWLSA-score: French and wolof lexicon-based for sentiment analysis. Proceedings of the 2019 5th International Conference on Information Management (ICIM), Cambridge, UK.","DOI":"10.1109\/INFOMAN.2019.8714667"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cogsys.2018.10.001","article-title":"Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information","volume":"54","author":"Alharbi","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.eswa.2018.10.002","article-title":"Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees","volume":"118","author":"Kraus","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.neucom.2018.11.084","article-title":"AELA-DLSTMs: Attention-enabled and location-aware double LSTMs for aspect-level sentiment classification","volume":"334","author":"Shuang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105443","DOI":"10.1016\/j.knosys.2019.105443","article-title":"Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification","volume":"193","author":"Zhao","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e5107","DOI":"10.1002\/cpe.5107","article-title":"Systematic literature review of sentiment analysis on Twitter using soft computing techniques","volume":"32","author":"Kumar","year":"2020","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., and Izumi, K. (2020, January 7\u201312). Word-level contextual sentiment analysis with interpretability. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.5845"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2019.10.009","article-title":"A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks","volume":"380","author":"Dashtipour","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neucom.2019.11.054","article-title":"BiLSTM with multi-polarity orthogonal attention for implicit sentiment analysis","volume":"383","author":"Wei","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_30","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 Gener. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cambria, E., Li, Y., Xing, F.Z., Poria, S., and Kwok, K. (2020, January 19\u201323). SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual Event.","DOI":"10.1145\/3340531.3412003"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s41019-020-00122-4","article-title":"Contextual sentiment neural network for document sentiment analysis","volume":"5","author":"Ito","year":"2020","journal-title":"Data Sci. Eng."},{"key":"ref_33","first-page":"1611","article-title":"Enhanced twitter sentiment analysis using hybrid approach and by accounting local contextual semantic","volume":"29","author":"Gupta","year":"2019","journal-title":"J. Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Santhiya, P., Kogilavani, S., and Malliga, S. (2021, January 2\u20134). Sentiment Analysis Classifiers for Polarity Detection in Social Media Text: A Comparative Study. Proceedings of the 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA52323.2021.9676111"},{"key":"ref_35","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":"Artif. Intell. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e12332","DOI":"10.1111\/exsy.12332","article-title":"Emotion-aware polarity lexicons for Twitter sentiment analysis","volume":"38","author":"Bandhakavi","year":"2021","journal-title":"Expert Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Koochari, A., Gharahbagh, A., and Hajihashemi, V. (2020, January 23\u201324). A Persian part of speech tagging system using the long short-term memory neural network. Proceedings of the 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran.","DOI":"10.1109\/ICSPIS51611.2020.9349556"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"54885","DOI":"10.1007\/s11042-023-17734-3","article-title":"Order-Sensitivity Sentiment dictionary of word sequences containing intensifiers","volume":"83","author":"Zargari","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102138","DOI":"10.1016\/j.artmed.2021.102138","article-title":"Aspect-based sentiment analysis with graph convolution over syntactic dependencies","volume":"119","author":"Corcoran","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cambria, E., Mao, R., Han, S., and Liu, Q. (December, January 28). Sentic parser: A graph-based approach to concept extraction for sentiment analysis. Proceedings of the 2022 IEEE International Conference on Data Mining Workshops (ICDMW), Orlando, FL, USA.","DOI":"10.1109\/ICDMW58026.2022.00060"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.ins.2021.01.025","article-title":"Sentiment analysis with genetic programming","volume":"562","author":"Junior","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhang, Q., and Si, L. (2019, January 13\u201317). Tweetsenti: Target-dependent tweet sentiment analysis. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3314141"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103058","DOI":"10.1016\/j.ipm.2022.103058","article-title":"A hybrid lexicon-based and neural approach for explainable polarity detection","volume":"59","author":"Polignano","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_44","first-page":"21","article-title":"Development of Sentiment Detection combined with Deep Learning and Sentiment Dictionary","volume":"9","author":"Kim","year":"2023","journal-title":"J. Internet Things Converg."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gupta, S., Singh, A., and Kumar, V. (2023). Emoji, text, and sentiment polarity detection using natural language processing. Information, 14.","DOI":"10.3390\/info14040222"},{"key":"ref_46","first-page":"965","article-title":"Classification of tweets data based on polarity using improved RBF kernel of SVM","volume":"15","author":"Gopi","year":"2023","journal-title":"Int. J. Inf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tong, X., Chen, M., and Feng, G. (2024). A Study on the Emotional Tendency of Aquatic Product Quality and Safety Texts Based on Emotional Dictionaries and Deep Learning. Appl. Sci., 14.","DOI":"10.3390\/app14052119"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ben, T.L., Alla, P.C.R., Komala, G., and Mishra, K. (2023, January 5\u20136). Detecting sentiment polarities with comparative analysis of machine learning and deep learning algorithms. Proceedings of the 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India.","DOI":"10.1109\/InCACCT57535.2023.10141741"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7617","DOI":"10.1109\/ACCESS.2023.3238207","article-title":"Semantic orientation of crosslingual sentiments: Employment of lexicon and dictionaries","volume":"11","author":"Raza","year":"2023","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ramos Magna, A., Zamora, J., and Allende-Cid, H. (2024). Senti-Sequence: Learning to Represent Texts for Sentiment Polarity Classification. Appl. Sci., 14.","DOI":"10.3390\/app14031033"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/s13278-024-01268-z","article-title":"LexiSNTAGMM: An unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approaches","volume":"14","author":"Bashiri","year":"2024","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Young, J.C., Arthur, R., and Williams, H.T. (2024). CIDER: Context-sensitive polarity measurement for short-form text. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0299490"},{"key":"ref_53","first-page":"100182","article-title":"Multi-lingual opinion mining for social media discourses: An approach using deep learning based hybrid fine-tuned smith algorithm with adam optimizer","volume":"3","author":"Shahade","year":"2023","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hansen, P.C. (1998). Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion, SIAM.","DOI":"10.1137\/1.9780898719697"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1007\/BF01937276","article-title":"The truncated SVD as a method for regularization","volume":"27","author":"Hansen","year":"1987","journal-title":"BIT Numer. Math."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"136683","DOI":"10.1016\/j.electacta.2020.136683","article-title":"On a variation of the Tikhonov regularization method for calculating the distribution function of relaxation times in impedance spectroscopy","volume":"354","author":"Gavrilyuk","year":"2020","journal-title":"Electrochim. Acta"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"109107","DOI":"10.1016\/j.measurement.2021.109107","article-title":"Acoustic tomography of two dimensional velocity field by using meshless radial basis function and modified Tikhonov regularization method","volume":"175","author":"Zhang","year":"2021","journal-title":"Measurement"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jiang, J., Tang, H., Mohamed, M.S., Luo, S., and Chen, J. (2020). Augmented tikhonov regularization method for dynamic load identification. Appl. Sci., 10.","DOI":"10.3390\/app10186348"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jnca.2017.11.001","article-title":"SentiRelated: A cross-domain sentiment classification algorithm for short texts through sentiment related index","volume":"101","author":"Wang","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_61","unstructured":"Taboada, M., Anthony, C., and Voll, K.D. (2006, January 22\u201328). Methods for Creating Semantic Orientation Dictionaries. Proceedings of the LREC, Genoa, Italy."},{"key":"ref_62","unstructured":"Stone, P.J., Dunphy, D.C., and Smith, M.S. (1966). The General Inquirer: A Computer Approach to Content Analysis, MIT Press."},{"key":"ref_63","unstructured":"Bradley, M.M., and Lang, P.J. (1999). Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings, University of Florida. Technical Report, Technical Report C-2."},{"key":"ref_64","unstructured":"Baccianella, S., Esuli, A., and Sebastiani, F. (2010, January 17\u201323). Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Lrec, Valletta, Malta."},{"key":"ref_65","unstructured":"Brooke, J. (2009). A Semantic Approach to Automated Text Sentiment Analysis. [Master\u2019s Thesis, Simon Fraser University]."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kiritchenko, S., and Mohammad, S.M. (2017). The effect of negators, modals, and degree adverbs on sentiment composition. arXiv.","DOI":"10.18653\/v1\/W16-0410"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TCSS.2021.3069413","article-title":"Feature-based twitter sentiment analysis with improved negation handling","volume":"8","author":"Gupta","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_68","unstructured":"Mohammad, S.M., Kiritchenko, S., and Zhu, X. (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sygkounas, E., Rizzo, G., and Troncy, R. (2016). Sentiment polarity detection from amazon reviews: An experimental study. Semantic Web Evaluation Challenge, Springer.","DOI":"10.1007\/978-3-319-46565-4_8"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Di Rosa, E., and Durante, A. (June, January 29). App2check extension for sentiment analysis of amazon products reviews. Proceedings of the Semantic Web Challenges: Third SemWebEval Challenge at ESWC 2016, Heraklion, Crete, Greece. Revised Selected Papers 3.","DOI":"10.1007\/978-3-319-46565-4_7"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Petrucci, G., and Dragoni, M. (2016). The IRMUDOSA system at ESWC-2016 challenge on semantic sentiment analysis. Semantic Web Evaluation Challenge, Springer.","DOI":"10.1007\/978-3-319-46565-4_10"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"11763","DOI":"10.3233\/JIFS-202879","article-title":"GINS: A Global intensifier-based N-Gram sentiment dictionary","volume":"40","author":"Zargari","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.eswa.2018.03.004","article-title":"Senti-N-Gram: An n-gram lexicon for sentiment analysis","volume":"103","author":"Dey","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Carta, S., Podda, A.S., Recupero, D.R., Saia, R., and Usai, G. (2020). Popularity prediction of instagram posts. Information, 11.","DOI":"10.20944\/preprints202008.0676.v1"},{"key":"ref_75","first-page":"137","article-title":"A novel accurate genetic algorithm for multivariable systems","volume":"5","author":"Gharahbagh","year":"2008","journal-title":"World Appl. Sci. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"15907","DOI":"10.1007\/s00521-023-08576-z","article-title":"MiMuSA\u2014Mimicking human language understanding for fine-grained multi-class sentiment analysis","volume":"35","author":"Wang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1007\/s12559-023-10227-3","article-title":"Optimizing sentiment analysis: A cognitive approach with negation handling via mathematical modelling","volume":"16","author":"Punetha","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"35457","DOI":"10.1007\/s11042-023-15140-3","article-title":"Deterministic solution of algebraic equations in sentiment analysis","volume":"82","author":"Jalali","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_79","unstructured":"Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., and Wilson, T. (2013). Semantic sentiment analysis of twitter. Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, Georgia, USA, 2013, Association for Computational Linguistics."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/513\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:05Z","timestamp":1760110925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/513"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,23]]},"references-count":79,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["info15090513"],"URL":"https:\/\/doi.org\/10.3390\/info15090513","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,8,23]]}}}