Classification Analysis of MotoGP Comments on Media Social Twitter Using Algorithm Support Vector Machine and Naive Bayes

Siswanto, Siswanto; Wibawa, Yuda Pratama; Gata, Windu; Gata, Grace; Kusumawardhani, Nia

doi:10.1109/icaiti.2018.8686751

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…Keseluruhan nilai akurasi, presisi, recall, dan AUC dari SVM mengungguli nilai akurasi, presisi, recall, dan AUC dari NB. Hal ini sejalan dengan kesimpulan penelitian Bryan Pratama [20] dan Siswanto [21]. Penggunaan PSO pada SVM dan PSO pada NB juga meningkatkan nilai akurasi, presisi, recall, dan AUC, meskipun hanya merupakan peningkatan yang kecil, dibandingkan dengan SVM tanpa PSO dan NB tanpa PSO.…”

Section: Hasil Dan Pembahasanunclassified

“…Bryan Pratama menyebutkan bahwa SVM memiliki performa yang lebih baik dibandingkan dengan NB dalam hal akurasi, presisi, dan recall[20]. Hal yang serupa juga disimpulkan Siswanto dimana SVM memberikan performa akurasi yang lebih baik dibandingkan dengan NB[21] serta oleh Hernawati yang menyimpulkan bahwa kombinasi SVM dan PSO memberikan akurasi yang lebih baik dibandingkan dengan SVM, NB, dan kombinasi NB dan PSO[22]. Sementara itu Nia Kusuma Wardhani menyimpulkan bahwa kombinasi NB dan PSO memberikan akurasi yang lebih baik dibandingkan dengan SVM, NB, dan kombinasi SVM dan PSO[23].…”

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Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO

Hafidz

Liliana

2021

RESTI

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On March 2020 World Health Organization (WHO) has declared Covid-19 as global pandemic. As special agency of United Nation who responsible for international public healthy, WHO has done various actions to reduce this pandemic spreading rate. However, the handling of Covid-19 by WHO is not free from a number of controversies that gave rise to criticism and public opinion on the Twitter platform. In this research, a machine learning based classifier model has been made to determine the opinion or sentiment of the tweet. The dataset used is a set of tweets containing the phrase WHO and Covid-19 in period of March 1st until May 6th 2020 consisting of 4000 tweets with positive sentiments and 4000 tweets with negative sentiments. The proposed classifier model combined Support Vector Machine (SVM), N-Gram and Particle Swarm Optimization (PSO). The classifier model performance is evaluated using the value of Accuracy, Precision, Recall, and Area Under ROC Curve (AUC). Based on experiments conducted, the combination of SVM, N-gram (bigram), and PSO produced a pretty good performance in classifying tweet sentiment with values of Accuracy 0,755, Precision 0,719, Recall 0,837, and AUC 0,844.

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Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO

Hafidz

Liliana

2021

RESTI

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“…Sentiment analysis of comments on Youtube on the Samsung Galaxy Z Flip 3 gadget concludes that the SVM algorithm has a better accuracy rate than NBC, which is 96.43% (Iskandar & Nataliani, 2021). Research on the sentiment analysis of MotoGP comments also places the SVM algorithm better than NBC with an accuracy rate of 95.50% (Siswanto, Wibawa, Gata, Gata, & Kusumawardhani, 2018). Other studies related to sentiment analysis also show that the SVM algorithm is better than NBC in the case of airline reviews (Rahat, Kahir, & Masum, 2019).…”

Section: Introductionmentioning

confidence: 96%

Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis

Sitepu

Munthe

Harahap

2022

SinkrOn

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As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.

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“…Research conducted by Siswanto, Wibawa, Gata, and Kusumawardhani in a paper published at the 2018 ICAITI conference discusses the analysis of the classification of comments related to MotoGP on Twitter using the Support Vector Machine and Naïve Bayes algorithms. The results show that the accuracy of the SVM algorithm is 95.50%, and Naïve Bayes has an accuracy of 93% [2].…”

Section: Introductionmentioning

confidence: 99%

“…The number of tourist destinations owned by the island of Lombok, from beach tourism to mountains, and now the construction of the MotoGP Circuit in Kuta Mandalika provides a positive trend for NTB tourism. The public showed enthusiasm by providing information and comments on social media about MotoGP [2].…”

Section: Introductionmentioning

confidence: 99%

Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method

Hammad

Hardita

Amrullah

2022

MATRIX

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The rapid and widespread dissemination of information currently affects the tourism sector. One tourist area that is quite widely discussed is the Mandalika Circuit. Twitter is one platform that provides comments related to the Mandalika Circuit. The amount of information related to the Mandalika Circuit is currently not being utilized properly by managers (government or private). It causes many topics related to the Mandalika Circuit that are currently trending, and public sentiment regarding the Mandalika Circuit is unknown to the government or private sector. Ignorance can result in delays in decision making which can harm the manager. To overcome this problem, research on sentiment analysis and topic modeling related to the Mandalika Circuit was carried out. The sentiment analysis method used is SVM and for modeling using LDA. Based on the results of the sentiment analysis, 1500 tweets were obtained before doing the pre-processing process, thus getting a dataset of 500 tweets divided into 398 positive and 102 negative tweets. So it can be concluded that more Twitter users give positive than negative responses to the Mandalika Circuit. The test results show that the SVM algorithm can classify sentiment toward the Mandalika Circuit well, as indicated by the measurement of the performance of the SVM algorithm, namely 87% accuracy, 77% precision, 84.81% recall, and 98.52% specificity. These results also show that the F1 Score compares the average precision and recall, which is weighted at 80.72%.

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Classification Analysis of MotoGP Comments on Media Social Twitter Using Algorithm Support Vector Machine and Naive Bayes

Cited by 8 publications

References 4 publications

Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO

Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO

Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis

Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method

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