Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches
AbstractWe 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. View Full-Text
Share & Cite This Article
Kapočiūtė-Dzikienė, J.; Damaševičius, R.; Woźniak, M. Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches. Computers 2019, 8, 4.
Kapočiūtė-Dzikienė J, Damaševičius R, Woźniak M. Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches. Computers. 2019; 8(1):4.Chicago/Turabian Style
Kapočiūtė-Dzikienė, Jurgita; Damaševičius, Robertas; Woźniak, Marcin. 2019. "Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches." Computers 8, no. 1: 4.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.