High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

Muflikhah, Lailil; Haryanto, Dimas Joko

doi:10.25126/jitecs.20183260

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…L. Muflikhah et al [20] compared the performance of Polynomial and Radial Basis Function (RBF) kernel in SVM in the sentiment analysis of online product reviews. Data were collected from 200 comments and preprocessed using tokenization, stop word removal, stemming, and normalization in data preprocessing.…”

Section: Related Workmentioning

confidence: 99%

“…The use of applicable kernel functions can drastically reduce computational efforts to make operations feasible. Researchers have employed kernel of SVM in their studies, such as Polynomial Kernel (PK) [20]- [22], Radial Basis Function Kernel (RBF) [20]- [23], Gaussian Kernel [21], Linear Kernel [21], Sigmoid Kernel [21], Laplacian Kernel [21], and Anova Kernel [21], but there are only a few researchers applying Normalized Poly Kernel despite its excellent performance [24] [25]. Thus, in this research, we employed a Normalized Poly Kernel in Support Vector Machine (SVM)…”

Section: Introductionmentioning

confidence: 99%

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Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

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Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

View full text Add to dashboard Cite

show abstract

“…Parameter optimization is time consuming if done manually, especially since it has many parameters. The biggest problem in setting up an SVM model is choosing kernel functions and their parameter values [32], [33]. Incorrect parameter settings lead to poor classification results.…”

Section: Resultsmentioning

confidence: 99%

Effective predictive modelling for coronary artery diseases using support vector machine

Nugroho

Sukmadewa

Vidianto

et al. 2022

IJ-AI

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Coronary artery disease (CAD) is a category of cardiovascular disease that causes the highest mortality rate in the world. CAD occurs due to plaque build-up on the walls of the arteries that supply blood to the heart and other organs of the body. To control the mortality rate, a practical model that is capable of predicting CAD is needed. Machine learning approaches have been used in solving various problems in various domains, including biomedicine. However, real-world data often has an unbalanced class distribution that can interfere with classifier performance. In addition, data has many features to process. This study focuses on effective modeling capable of predicting CAD using feature selection to handle high dimensional data and feature resampling to handle unbalanced data. Feature selection is very effective by eliminating irrelevant features from the training data. Hyperparameter tuning is also done to find the best combination of parameters in support vector machines (SVM). Our results show that the SVM cross-validated ten times has a more accurate training result. Furthermore, the grid search on SVM cross-validated ten times had more accurate training model results and achieved 88% accuracy on the test data.

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“…5. Self-driving 5 : extremely imbalanced five-class dataset. There are tweets labeled with 'not relevant' in the original dataset.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…F1 can be used in a multi-class classification problem using the macro-averaging [29]. The F1 score for a binary problem is formulated in (5). The macro-averaging of F1 is shown in (6).…”

Section: Performance Evaluationmentioning

confidence: 99%

Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis

Cahya

Bachtiar

Mahmudy

2021

JITeCS

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The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.

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High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

Cited by 9 publications

References 12 publications

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Effective predictive modelling for coronary artery diseases using support vector machine

Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis

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