Event Classification in Surabaya on Twitter with Support Vector Machine

Ajipangestu, Drajad Bima; Sarno, Riyanarto

doi:10.1109/isemantic50169.2020.9234205

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The concept of SVM can be simply explained as trying to find the best hyperplane as a separator between two classes in the input space. [10]. The basic principle of SVM is a linear classifier, and it is more used to deal with nonlinear problems [11].…”

Section: Support Vector Machinementioning

confidence: 99%

“…Feature data is obtained by dividing each word to get a feature value per word, then the results of the pieces are averaged to one value in each review. The classification used in this study is to use SVM with 3 modifications to the parameters of the SVM method, which is C-SVC, SVC Linear, SVCnu with various kernel changes to get the best results [10].…”

Section: Sentiment Classification Svmmentioning

confidence: 99%

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Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Ajipangestu¹,

Sarno²

2022

Proceedings of the 3rd International Conference on Business and Management of Technology (ICONBMT 2021)

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Food is always close to us, along with the growing population, the food business will continue to grow. During the Covid-19 Pandemic, even though many people stay at home, they occasionally want to buy their favorite food outside the home, whether it's buying directly onsite or online. The aspect categorization is carried out by combining the LDA method and Semantic Similarity to categorize tweets into four culinary aspects (1) Price, (2) Taste, (3) Place, and (4) Service. The best performance of aspect categorization is by combining the LDA and TF-ICF 100% for term extension. Next, the classification stage with Word Embedding to extract features using GloVe and SVM with three-parameter modifications of the SVC method: (1) C-SVC, (2) Linear SVC, and (3) SVCnu with various kernel changes to get the best results. Then an increase in classification accuracy is carried out using SentiCircle. The results of this study show that aspects of service (4) Has a review with a high negative sentiment that reaches 10.869% compared to sentiment reviews on other aspects in percent (price: 4.348, taste: 6.522, place: 4.521) so that business owners culinary needs to make improvements to pay more attention to customer service to reduce the number of negative reviews on this service aspect. The results also show that changes in sentiment (on positive or negative sentiment) are influenced by the aspect of each review.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Sentiment Classification Svmmentioning

confidence: 99%

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Ajipangestu¹,

Sarno²

2022

Proceedings of the 3rd International Conference on Business and Management of Technology (ICONBMT 2021)

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method produces 88.52% accuracy when classifying twitter data [3]. In addition, research conducted by [4] also said that SVM can perform data classification very well compared to conventional methods such as artificial neural network methods. The SVM concept can help in finding the best hyperplane that serves as a separator between the two data label classes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Styawati,

Isnain,

Hendrastuty

et al. 2021

JPIT

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Twitter is a social media that is widely used by the public. Twitter social media can be used to express opinions or opinions about an object. This shows that there is a huge opportunity for data sources, so they can be used for sentiment analysis. There are many algorithms for performing sentiment analysis, including Support Vector Machine (SVM) and Naive Bayes (NB). Because of the many opinions regarding the performance of the two methods, the researcher is interested in classifying the data using the SVM and NB methods. The data used in this study is data on public opinion regarding the Covid-19 vaccination policy. The first classification process is carried out by the SVM method using various kernels. After getting the highest accuracy result, then the accuracy result is compared with the accuracy value from the NB method classification results.

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An Investigative Approach to Employ Support Vector Classifier as a Potential Detector of Brain Cancer from MRI Dataset

Nishat

Faisal

Hasan

et al. 2021

2021 International Conference on Electronics, Communications and Information Technology (ICECIT)

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Event Classification in Surabaya on Twitter with Support Vector Machine

Cited by 3 publications

References 17 publications

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

An Investigative Approach to Employ Support Vector Classifier as a Potential Detector of Brain Cancer from MRI Dataset

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