{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:16:15Z","timestamp":1772262975853,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Operational Program Competitiveness, Entrepreneurship and Innovation of Greece, call RESEARCH - CREATE - INNOVATE","award":["T1EDK-03470"],"award-info":[{"award-number":["T1EDK-03470"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.<\/jats:p>","DOI":"10.3390\/info12080331","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T10:54:35Z","timestamp":1629284075000},"page":"331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-8292","authenticated-orcid":false,"given":"Georgios","family":"Alexandridis","sequence":"first","affiliation":[{"name":"Department of Cultural Technology and Communication, University of the Aegean, 81100 Mitilini, Greece"},{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-8167","authenticated-orcid":false,"given":"Iraklis","family":"Varlamis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece"}]},{"given":"Konstantinos","family":"Korovesis","sequence":"additional","affiliation":[{"name":"Palo Services Ltd., 10562 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9884-935X","authenticated-orcid":false,"given":"George","family":"Caridakis","sequence":"additional","affiliation":[{"name":"Department of Cultural Technology and Communication, University of the Aegean, 81100 Mitilini, Greece"}]},{"given":"Panagiotis","family":"Tsantilas","sequence":"additional","affiliation":[{"name":"Palo Services Ltd., 10562 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, W., Xu, M., and Jiang, Q. (2018, January 15\u201320). Opinion mining and sentiment analysis in social media: Challenges and applications. Proceedings of the International Conference on HCI in Business, Government, and Organizations, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-91716-0_43"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Soong, H.C., Jalil, N.B.A., Ayyasamy, R.K., and Akbar, R. (2019, January 27\u201328). The essential of sentiment analysis and opinion mining in social media: Introduction and survey of the recent approaches and techniques. Proceedings of the 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Kota Kinabalu, Malaysia.","DOI":"10.1109\/ISCAIE.2019.8743799"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Samal, B., Behera, A.K., and Panda, M. (2017, January 4\u20135). Performance analysis of supervised machine learning techniques for sentiment analysis. Proceedings of the 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS), Chennai, India.","DOI":"10.1109\/SSPS.2017.8071579"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Katakis, I.M., Varlamis, I., and Tsatsaronis, G. (2014, January 14\u201318). Pythia: Employing lexical and semantic features for sentiment analysis. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Nancy, France.","DOI":"10.1007\/978-3-662-44845-8_32"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5995","DOI":"10.1016\/j.eswa.2014.03.022","article-title":"Feature-based opinion mining through ontologies","volume":"41","author":"Moreno","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maxwell, M., and Hughes, B. (2006, January 15\u201316). Frontiers in linguistic annotation for lower-density languages. Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006, Sydney, Australia.","DOI":"10.3115\/1641991.1641996"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhou, H., Chen, L., Shi, F., and Huang, D. (2015, January 26\u201331). Learning bilingual sentiment word embeddings for cross-language sentiment classification. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China.","DOI":"10.3115\/v1\/P15-1042"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, K., and Wan, X. (2017, January 7\u201311). Towards a universal sentiment classifier in multiple languages. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1053"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.inffus.2015.06.002","article-title":"Opinion mining and information fusion: A survey","volume":"27","author":"Balazs","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_10","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_11","unstructured":"Taher, S.A., Akhter, K.A., and Hasan, K.A. (2018, January 21\u201322). N-gram based sentiment mining for bangla text using support vector machine. Proceedings of the 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3389\/fams.2018.00041","article-title":"Text classification using the n-gram graph representation model over high frequency data streams","volume":"4","author":"Violos","year":"2018","journal-title":"Front. Appl. Math. Stat."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Skianis, K., Malliaros, F., and Vazirgiannis, M. (2018, January 6). Fusing document, collection and label graph-based representations with word embeddings for text classification. Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12), New Orleans, LA, USA.","DOI":"10.18653\/v1\/W18-1707"},{"key":"ref_14","unstructured":"Maas, A., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., and Potts, C. (2011, January 19\u201324). Learning word vectors for sentiment analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kwon, H.J., Ban, H.J., Jun, J.K., and Kim, H.S. (2021). Topic modeling and sentiment analysis of online review for airlines. Information, 12.","DOI":"10.3390\/info12020078"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"76","DOI":"10.5614\/itbj.ict.res.appl.2016.10.1.6","article-title":"Topic Modeling in Sentiment Analysis: A Systematic Review","volume":"10","author":"Rana","year":"2016","journal-title":"J. ICT Res. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., and Qin, B. (2014, January 23\u201325). 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_18","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, U., and Polosukhin, I. (2017, January 4\u20139). Attention is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."},{"key":"ref_19","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the NAACL-HLT, Minneapolis, MN, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ethayarajh, K. (2019). How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings. arXiv.","DOI":"10.18653\/v1\/D19-1006"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Budzianowski, P., and Vuli\u0107, I. (2019). Hello, it\u2019s GPT-2\u2013how can I help you? towards the use of pretrained language models for task-oriented dialogue systems. arXiv.","DOI":"10.18653\/v1\/D19-5602"},{"key":"ref_22","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2021, July 01). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/www.cs.ubc.ca\/~amuham01\/LING530\/papers\/radford2018improving.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Papantoniou, K., and Tzitzikas, Y. (2020, January 2\u20134). NLP for the Greek Language: A Brief Survey. Proceedings of the 11th Hellenic Conference on Artificial Intelligence, Athens, Greece.","DOI":"10.1145\/3411408.3411410"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nikiforos, M.N., Voutos, Y., Drougani, A., Mylonas, P., and Kermanidis, K.L. (2021). The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications. Data, 6.","DOI":"10.3390\/data6050052"},{"key":"ref_25","unstructured":"GitHub (2021, July 01). Skroutz\/Greek_Stemmer: A Simple Greek Stemming Library. Available online: https:\/\/github.com\/skroutz\/greek_stemmer."},{"key":"ref_26","unstructured":"Ntais, G. (2006). Development of a Stemmer for the Greek Language. [Master\u2019s Thesis, Department of Computer and Systems Sciences, Stockholm University\/Royal Institute of Technology]."},{"key":"ref_27","unstructured":"Prokopidis, P., Desipri, E., Koutsombogera, M., Papageorgiou, H., and Piperidis, S. (2005, January 9\u201310). Theoretical and practical issues in the construction of a Greek dependency treebank. Proceedings of the 4th Workshop on Treebanks and Linguistic Theories (TLT 2005), Barcelona, Spain."},{"key":"ref_28","unstructured":"AUEB (2021, July 01). NLP Group. Available online: http:\/\/nlp.cs.aueb.gr\/software.html."},{"key":"ref_29","unstructured":"Nikiforos, M.N., and Kermanidis, K.L. (2020, January 11\u201316). A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web. Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1142\/S0218213007003680","article-title":"Named entity recognition in greek texts with an ensemble of svms and active learning","volume":"16","author":"Lucarelli","year":"2007","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_31","first-page":"3","article-title":"PaloPro: A platform for knowledge extraction from big social data and the news","volume":"4","author":"Makrynioti","year":"2017","journal-title":"Int. J. Big Data Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"171","DOI":"10.24297\/ijct.v2i3c.2717","article-title":"Opinion mining and sentiment analysis: A survey","volume":"2","author":"Sadegh","year":"2012","journal-title":"Int. J. Comput. Technol."},{"key":"ref_33","first-page":"89","article-title":"The software infrastructure for the development and validation of the Greek WordNet","volume":"7","author":"Grigoriadou","year":"2004","journal-title":"Rom. J. Inf. Sci. Technol."},{"key":"ref_34","unstructured":"BalkaNet (2021, July 01). Project Home Page. Available online: http:\/\/www.dblab.upatras.gr\/balkanet\/."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Guo, X., and Li, J. (2019, January 22\u201325). A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency. Proceedings of the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain.","DOI":"10.1109\/SNAMS.2019.8931720"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Petasis, G., Spiliotopoulos, D., Tsirakis, N., and Tsantilas, P. (2014, January 15\u201317). Sentiment analysis for reputation management: Mining the greek web. Proceedings of the Hellenic Conference on Artificial Intelligence, Ioannina, Greece.","DOI":"10.1007\/978-3-319-07064-3_26"},{"key":"ref_37","unstructured":"Petasis, G., Karkaletsis, V., Paliouras, G., Androutsopoulos, I., and Spyropoulos, C.D. (2002). Ellogon: A new text engineering platform. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Prokopidis, P., and Piperidis, S. (2020, January 2\u20134). A Neural NLP toolkit for Greek. Proceedings of the 11th Hellenic Conference on Artificial Intelligence, Athens, Greece.","DOI":"10.1145\/3411408.3411430"},{"key":"ref_39","unstructured":"Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O\u2019Reilly Media, Inc."},{"key":"ref_40","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2021, July 01). SpaCy: Industrial-Strength Natural Language Processing in Python. Available online: https:\/\/zenodo.org\/record\/5115698#.YRnUSEQzZPY."},{"key":"ref_41","unstructured":"Apache Software Foundation (2021, July 01). OpenNLP Natural Language Processing Library. Available online: http:\/\/opennlp.apache.org\/."},{"key":"ref_42","unstructured":"GitHub (2021, July 01). Eellak\/Gsoc2018-Spacy: [GSOC] Greek Language Support for Spacy.io Python NLP Software. Available online: https:\/\/github.com\/eellak\/gsoc2018-spacy."},{"key":"ref_43","unstructured":"CLARIN ERIC (2021, July 01). Part-of-Speech Taggers and Lemmatizers. Available online: https:\/\/www.clarin.eu\/resource-families\/tools-part-speech-tagging-and-lemmatization."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wo\u0142k, K. (2021). Real-Time Sentiment Analysis for Polish Dialog Systems Using MT as Pivot. Electronics, 10.","DOI":"10.3390\/electronics10151813"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"\u0160trimaitis, R., Stefanovi\u010d, P., Ramanauskait\u0117, S., and Slotkien\u0117, A. (2021). Financial Context News Sentiment Analysis for the Lithuanian Language. Appl. Sci., 11.","DOI":"10.3390\/app11104443"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pecar, S., \u0160imko, M., and Bielikova, M. (2019, January 2). Improving Sentiment Classification in Slovak Language. Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, Florence, Italy.","DOI":"10.18653\/v1\/W19-3716"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kalamatianos, G., Mallis, D., Symeonidis, S., and Arampatzis, A. (2015, January 1\u20133). Sentiment analysis of greek tweets and hashtags using a sentiment lexicon. Proceedings of the 19th Panhellenic Conference on Informatics, Athens, Greece.","DOI":"10.1145\/2801948.2802010"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s10579-018-9420-4","article-title":"Building and evaluating resources for sentiment analysis in the Greek language","volume":"52","author":"Tsakalidis","year":"2018","journal-title":"Lang. Resour. Eval."},{"key":"ref_49","unstructured":"Outsios, S., Karatsalos, C., Skianis, K., and Vazirgiannis, M. (2020, January 11\u201316). Evaluation of Greek Word Embeddings. Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France."},{"key":"ref_50","unstructured":"(2021, July 01). Greek Word2Vec. Available online: http:\/\/archive.aueb.gr:7000\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.eswa.2016.10.043","article-title":"Sentiment analysis leveraging emotions and word embeddings","volume":"69","author":"Giatsoglou","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_52","unstructured":"Fares, M., Kutuzov, A., Oepen, S., and Velldal, E. (2017, January 22\u201324). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. Proceedings of the 21st Nordic Conference on Computational Linguistics, Gothenburg, Sweden."},{"key":"ref_53","unstructured":"Grave, E., Bojanowski, P., Gupta, P., Joulin, A., and Mikolov, T. (2018, January 7\u201312). Learning Word Vectors for 157 Languages. Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Koutsikakis, J., Chalkidis, I., Malakasiotis, P., and Androutsopoulos, I. (2020, January 2\u20134). Greek-bert: The greeks visiting sesame street. Proceedings of the 11th Hellenic Conference on Artificial Intelligence, Athens, Greece.","DOI":"10.1145\/3411408.3411440"},{"key":"ref_55","unstructured":"Su\u00e1rez, P.J.O., Sagot, B., and Romary, L. (2019, January 22). Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures. Proceedings of the 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7), Cardiff, UK."},{"key":"ref_56","unstructured":"(2021, July 01). Common Crawl. Available online: http:\/\/commoncrawl.org\/."},{"key":"ref_57","unstructured":"(2021, July 01). Hugging Face. Available online: https:\/\/huggingface.co\/nikokons\/gpt2-greek."},{"key":"ref_58","unstructured":"Esuli, A., and Sebastiani, F. (2006, January 5\u20136). Determining term subjectivity and term orientation for opinion mining. Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1145\/361219.361220","article-title":"A vector space model for automatic indexing","volume":"18","author":"Salton","year":"1975","journal-title":"Commun. ACM"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hofmann, T. (1999, January 15\u201319). Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA.","DOI":"10.1145\/312624.312649"},{"key":"ref_61","first-page":"19","article-title":"Graph based representation and analysis of text document: A survey of techniques","volume":"96","author":"Sonawane","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., and Sun, M. (2020). Representation Learning and NLP. Representation Learning for Natural Language Processing, Springer.","DOI":"10.1007\/978-981-15-5573-2"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., and Zhai, C. (2012). A survey of text classification algorithms. Mining Text Data, Springer.","DOI":"10.1007\/978-1-4614-3223-4"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Vijayan, V.K., Bindu, K., and Parameswaran, L. (2017, January 13\u201316). A comprehensive study of text classification algorithms. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8125990"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., and Brown, D. (2019). Text classification algorithms: A survey. Information, 10.","DOI":"10.3390\/info10040150"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ijresmar.2018.09.009","article-title":"Comparing automated text classification methods","volume":"36","author":"Hartmann","year":"2019","journal-title":"Int. J. Res. Mark."},{"key":"ref_67","first-page":"221","article-title":"Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification","volume":"5","year":"2017","journal-title":"Balt. J. Mod. Comput."},{"key":"ref_68","unstructured":"FastText (2021, July 01). Word Vectors for 157 Languages. Available online: https:\/\/fasttext.cc\/docs\/en\/crawl-vectors.html."},{"key":"ref_69","unstructured":"GitHub (2021, July 01). Nlpaueb\/Greek-Bert: A Greek Edition of BERT Pre-Trained Language Model. Available online: https:\/\/github.com\/nlpaueb\/greek-bert."},{"key":"ref_70","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_71","unstructured":"HPC (2021, July 01). National HPC Infrastructure. Available online: https:\/\/hpc.grnet.gr\/en\/."},{"key":"ref_72","unstructured":"(2021, July 01). Hugging Face. Available online: https:\/\/huggingface.co\/gealexandri\/palobert-base-greek-uncased-v1."},{"key":"ref_73","unstructured":"(2021, July 01). Hugging Face. Available online: https:\/\/huggingface.co\/gealexandri\/greeksocialbert-base-greek-uncased-v1."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1109\/TKDE.2016.2597848","article-title":"Managing diverse sentiments at large scale","volume":"28","author":"Tsytsarau","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/3449049","article-title":"The best of NLP","volume":"64","author":"Edwards","year":"2021","journal-title":"Commun. ACM"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/8\/331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:46:10Z","timestamp":1760165170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/8\/331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,18]]},"references-count":75,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["info12080331"],"URL":"https:\/\/doi.org\/10.3390\/info12080331","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,18]]}}}