scholarly journals Cross-domain sentiment analysis model on Indonesian YouTube comment

2021 ◽  
Vol 7 (1) ◽  
pp. 12
Author(s):  
Agus Sasmito Aribowo ◽  
Halizah Basiron ◽  
Noor Fazilla Abd Yusof ◽  
Siti Khomsah
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Machine Learning Method ◽  
Learning Method ◽  
Analysis Model ◽  
Language Form ◽  
Cross Domain ◽  
Ensemble Machine Learning ◽  
Stop Word

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.

scholarly journals Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information

2021 ◽  
Vol 5 (2) ◽  
pp. 415
Author(s):  
Firdausi Nuzula Zamzami ◽  
Adiwijaya Adiwijaya ◽  
Mahendra Dwifebri P
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Mutual Information ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Internet Information

Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.

Estimations of Long-Term nss-SO42– and NO3– Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method

2020 ◽  
Vol 54 (18) ◽  
pp. 11118-11126
Author(s):  
Xingcheng Lu ◽  
Dehao Yuan ◽  
Yiang Chen ◽  
Jimmy C.H. Fung ◽  
Wenkai Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
East Asia ◽  
Machine Learning Method ◽  
Learning Method ◽  
Ensemble Machine Learning

scholarly journals Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

2020 ◽  
Vol 10 (11) ◽  
pp. 4016 ◽  
Author(s):  
Xudong Hu ◽  
Han Zhang ◽  
Hongbo Mei ◽  
Dunhui Xiao ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Southwest China ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Measures ◽  
Ensemble Machine Learning

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

scholarly journals Application of Twin Objective Function SVM in Sentiment Analysis

2020 ◽  
Author(s):  
Qiaoman Yang ◽  
Chunyu Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Key Issues ◽  
Single Objective ◽  
Accuracy And Stability

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.

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