Sentiment analysis of Indonesian police chief using multi-level ensemble model

Satrya, Wahyu Fadli; Aprilliyani, Ria; Yossy, Emny Harna

doi:10.1016/j.procs.2022.12.177

Cited by 11 publications

(8 citation statements)

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“…The ensemble learning method improves classification accuracy for multiclass segmentation [14]. Research [15], [16] confirmed that the selection of algorithms for ensemble learning affects the accuracy results and the need to use customized feature extraction to improve accuracy performance.…”

Section: Introductionmentioning

confidence: 89%

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Ningsih,

Wibowo,

Dullah

et al. 2023

J. Soft Comput. Explor.

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The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.

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Section: Introductionmentioning

confidence: 89%

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Ningsih,

Wibowo,

Dullah

et al. 2023

J. Soft Comput. Explor.

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“…Pada penelitian yang dilakukan Satrya, dkk [4], mengusulkan model ansambel tingkat kedua yang disebut model ansambel multi-level untuk analisis sentimen. Ini menggabungkan Multinomial Naive Bayes, pembelajaran ansambel berbasis Boosting, dan pembelajaran ansambel berbasis Bagging.…”

Section: IIunclassified

Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine

Saddam,

Kurniawan D,

Indra

2023

JPIT

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Termination of employment (PHK) on a large scale has a very significant impact on society and the economy. Mass layoffs have led to an increase in the number of unemployed people. Many people who have lost their jobs without a stable source of income struggle to find new jobs. This exacerbated the situation on the labor market and increased the number of unemployed people. Mass layoffs can also reduce economic activity and consumption. The sentiment analysis carried out aims to determine public sentiment regarding the phenomenon of mass layoffs that are currently happening in Indonesia based on positive and negative categories. In this study, the classification method used is the SVM method, which is one of the supervised learning methods in machine learning and also uses Nave Bayes as a comparison method. After classification, the next stage is the testing process using the K-fold cross-validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 108 positive sentiments and 333 negative sentiments related to mass layoffs, while the results obtained from the test results using the SVM method show an accuracy of up to 84% while using the Nave Bayes method shows an accuracy of up to 74.1 percent

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“…Sentiment analysis is a field of Natural Language Processing (NLP) that analyzes an opinion or information in text form [3]. Starting from unstructured data, then transformed into more structured data to produce opinion classes, such as positive class, negative class, or neutral class [4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sentiment Analysis Accuracy in Delivery Services Through Synthetic Minority Oversampling Technique

Sri Anggraeni,

Andryana

2024

jitk

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Paxel is one of the delivery services that use the application. On Google Play, there are more than 10 thousand users leaving reviews. From this review data, a sentiment analysis was then carried out to determine the level of user satisfaction with Paxel's services. The methods used in this study are Random Forest (RF) and Support Vector Machine (SVM), as well as applying Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. The results showed that the method testing by dividing the data into two, namely training data and testing data by 80:20, stated that by applying the SMOTE, a higher accuracy value was obtained, where the accuracy of the RF method reached 91%, and the SVM method reached 87%. The level of user satisfaction with Paxel services tends to be neutral. This can be seen in the classification of the RF method with F1-Score values for the Positive class 89%, Neutral class 93%, and Negative class 92%.

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Sentiment analysis of Indonesian police chief using multi-level ensemble model

Cited by 11 publications

References 0 publications

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine

Enhancing Sentiment Analysis Accuracy in Delivery Services Through Synthetic Minority Oversampling Technique

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