Cross-domain sentiment analysis model on Indonesian YouTube comment

(1) * Agus Sasmito Aribowo Mail (Universitas Pembangunan Nasional "Veteran" Yogyakarta Indonesia, Indonesia)
(2) Halizah Basiron Mail (Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Noor Fazilla Abd Yusof Mail (Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Siti Khomsah Mail (Insitut Teknologi Telkom Purwokerto, Indonesia)
*corresponding author


A cross-domain sentiment analysis (CDSA) study in the Indonesian language and tree-based ensemble machine learning is quite interesting. CDSA is useful to support the labeling process of cross-domain sentiment and reduce any dependence on the experts; however, the mechanism in the opinion unstructured by stop word, language expressions, and Indonesian slang words is unidentified yet. This study aimed to obtain the best model of CDSA for the opinion in Indonesia language that commonly is full of stop words and slang words in the Indonesian dialect. This study was purposely to observe the benefits of the stop words cleaning and slang words conversion in CDSA in the Indonesian language form. It was also to find out which machine learning method is suitable for this model. This study started by crawling five datasets of the comments on YouTube from 5 different domains. The dataset was copied into two groups: the dataset group without any process of stop word cleaning and slang word conversion and the dataset group to stop word cleaning and slang word conversion. CDSA model was built for each dataset group and then tested using two types of tree-based ensemble machine learning, i.e., Random Forest (RF) and Extra Tree (ET) classifier, and tested using three types of non-ensemble machine learning, including Naïve Bayes (NB), SVM, and Decision Tree (DT) as the comparison. Then, It can be suggested that the accuracy of CDSA in Indonesia Language increased if it still removed the stop words and converted the slang words. The best classifier model was built using tree-based ensemble machine learning, particularly ET, as in this study, the ET model could achieve the highest accuracy by 91.19%. This model is expected to be the CDSA technique alternative in the Indonesian language.


Cross-domain; Sentiment analysis; Tree-based ensemble ML; Remove stop word; Convert slang word



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