{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:23:13Z","timestamp":1783009393338,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Na\u00efve Bayes Multinomial\u2014NBM and Support Vector Machine\u2014SVM) and deep learning (Long Short-Term Memory\u2014LSTM and Convolutional Neural Network\u2014CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive\/negative\/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.<\/jats:p>","DOI":"10.3390\/computers8010004","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T03:36:30Z","timestamp":1546486590000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches"],"prefix":"10.3390","volume":"8","author":[{"given":"Jurgita","family":"Kapo\u010di\u016bt\u0117-Dzikien\u0117","sequence":"first","affiliation":[{"name":"Faculty of Informatics, Vytautas Magnus University, K. Donelai\u010dio 58, 44248 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Kaunas University of Technology, K. Donelai\u010dio 73, 44249 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-5347","authenticated-orcid":false,"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,1]]},"reference":[{"key":"ref_1","first-page":"28","article-title":"Tweets sentiment analysis for healthcare on big data processing and IoT architecture using maximum entropy classifier","volume":"Volume 744","author":"Zin","year":"2019","journal-title":"Proceedings of the International Conference on Big Data Analysis and Deep Learning Applications"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moe, Z.H., San, T., Tin, H.M., Hlaing, N.Y., and Tin, M.M. (2019). 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