The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

Wijaya, Komang Dhiyo Yonatha; Karyawati, Anak Agung Istri Ngurah Eka

doi:10.24843/jlk.2020.v09.i02.p01

Cited by 5 publications

(4 citation statements)

References 0 publications

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“…The study was conducted on different numbers of labels, specifically 2 and 6, and the findings from the first experiment test to compare three SVM kernels on the 2 labels are presented in Table IV. The information presented in Table IV showed that the linear kernel performed better than the others in line with the results of earlier studies that examined these three kernels [68]. This was confirmed by the 79% accuracy recorded for linear compared to 76% for RBF and the lowest, 58%, for the polynomial.…”

Section: Hybrid Kernel Svmsupporting

confidence: 87%

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Zamsuri,

Defit,

Nurcahyo

2024

JAIT

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Emotion is an individual's response to an event or situation. Research related to emotions in the field of data science falls under sentiment analysis, where sentiment analysis mostly focuses on determining the positive or negative emotional tone. This study attempts to classify emotions into labels such as Happy, Love, Surprise, Anger, Fear, and Sadness. Then, these six emotion labels are further categorized into positive and negative groups. The dataset for this research comprises tweets from Twitter related to the 2024 presidential election in Indonesia. Several machine learning algorithms are employed in this study, including Naï ve Bayes (Multinomial Bayes, Bernoulli Bayes, Complement Bayes), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), comparing two feature extraction methods: Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). The resultsshow that the use of SVM does not yield better accuracy, whether using TF-IDF or BoW. Therefore, this study improves the accuracy of the SVM algorithm by combining kernels available in SVM (Polynomial, Radial Basis Function (RBF), and Linear). From the conducted experiments, it is evident that SVM with a combined kernel, referred to as SVM Polynomial, RBF, Linier (PoRLi) in this study, achieves better accuracy compared to a single kernel. This is reflected in the classification accuracy with 6 labels, reaching 62%, indicating a 2% increase from SVM Linear, which obtained the highest accuracy using a single kernel. Furthermore, in the classification of 2 labels, there is also a 1% increase when using SVM PoRLi. It can be concluded that SVM PoRLi can enhance accuracy across various labels.

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Section: Hybrid Kernel Svmsupporting

confidence: 87%

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Zamsuri,

Defit,

Nurcahyo

2024

JAIT

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“…Penelitian Pooja dan Bhalla R [8] juga mengatakan algoritma yang biasanya digunakan dalam klasifikasi dan regresi adalah Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Artifcial Neural Network (ANN), dan Linear Regressions (LR). Wijaya K dan Karyawatia A [9] melakukan penelitian tentang pengaruh dari penggunakan kernel pada algortima SVM terhadap sentimen yang akan dianalisis.…”

Section: Pendahuluanunclassified

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Fremmuzar,

Baita

2023

Komputika

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Telkomsel is an internet service provider in Indonesia which was launched in 1995. As an internet service provider with the most users, Telkomsel has become the center of attention of internet users in Indonesia. This invites user opinions and perspectives on Telkomsel, which is commonly referred to as sentiment. One of the media commonly used to express an opinion and point of view is Twitter. Twitter is a social media platform that is often a place for sharing and spreading the news, and discussing ideas, and opinions of Twitter users. In this study, the algorithm used is the Support Vector Machine. In the Support Vector Machine, there is a kernel trick that will be used to determine kernel performance and analyze sentiment. The sentiments analyzed amounted to 537 tweets collected by scraping. The collected tweets will go through the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, stemming, stopword removal, and detokenizing. A sentiment is classified into 2 labels, namely positive and negative. Based on the test results, the sigmoid kernel has the best performance with an accuracy value of 0.950, a precision of 0.945, a recall of 0.860, an f1-score of 0.896, and sentiment tend toward negative.

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“…SVM can be extended to draw a non-linear decision boundary by transforming the input from the original space to a high-dimensional space. Since the relationship between the input space and the transformation space is non-linear, the goal is to obtain a non-linear decision boundary [13,14]. To improve the accuracy of the problem, SVM has a kernel trick that can help solve the problem of changing data into non-linear space.…”

Section: Kernel Trickmentioning

confidence: 99%

“…To improve the accuracy of the problem, SVM has a kernel trick that can help solve the problem of changing data into non-linear space. In general, some of the most commonly used kernel functions in SVM are as follows [14,13]:…”

Section: Kernel Trickmentioning

confidence: 99%

Credit Risk Management Prediction Using the Support Vector Machine (SVM) Algorithm

Setiawan¹,

Martaseli²,

Tugiman³

et al. 2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and

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Information sharing throughout the globe or universe has become a characteristic of social media. There has been a lot of research into the classification of sentiments. In this study, Twitter has been mined for unstructured Gofood Reviews data. It has been preprocessed to analyze the reviews' sentiment with polarity analysis, feature extraction with TF-IDF, and supervised learning with random forest. From June 1, 2022 to June 30, 2022, a total of 28763 tweets with the keyword "Gofood" were retrieved from Twitter. The data is processed by the Python programming language utilizing NLTK, Sastrawi for the Indonesian language, Textblob, TF-IDF, Random Forest Classification, and other algorithms. Twitter is a nearly limitless source for classifying text. This algorithm takes roughly five minutes to computer.

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The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

Cited by 5 publications

References 0 publications

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Credit Risk Management Prediction Using the Support Vector Machine (SVM) Algorithm

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