Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/61081
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Kapočiūtė-Dzikienė, Jurgita;Damaševičius, Robertas;Wozniak, Marcin
Title: Sentiment analysis of Lithuanian texts using traditional and deep learning approaches
Is part of: Computers. Basel : MDPI, 2019, Vol. 8, iss. 1
Extent: p. 1-16
Date: 2019
Note: (This article belongs to the Special Issue Selected Papers from the 24th International Conference on Information and Software Technologies (ICIST 2018))
Keywords: Sentimentų analizė;Mašininis mokymas;Gilusis mokymas;Neuroniniai žodžių įterpiniai;Interneto komentarai;Lietuvių kalba;Sentiment analysis;Machine learning;Deep learning;Neural word embeddings;Internet comments;Lithuanian language
Abstract: We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) 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
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/61081/2/ISSN2073-431X_2019_V_8_1.PG_1-16.pdf
https://hdl.handle.net/20.500.12259/61081
https://doi.org/10.3390/computers8010004
Affiliation(s): Kauno technologijos universitetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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