Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods

Pratama, Bryan; Saputra, Dedi Dwi; Novianti, Deny; Purnamasari, Endah Dewi; Kuntoro, Antonius Yadi; Hermanto, -; Gata, Windu; Wardhani, Nia Kusuma; Sfenrianto, Sfenrianto; Budilaksono, Sularso

doi:10.1088/1742-6596/1201/1/012038

Cited by 13 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

“…Beberapa penelitian analisis sentimen sebelumnya banyak menggunakan algoritma SVM dan Naïve Bayes (NB) sebagai algoritma klasifikasinya. 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].…”

unclassified

See 1 more Smart Citation

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

Hafidz

Liliana

2021

RESTI

View full text Add to dashboard Cite

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.

show abstract

Section: Hasil Dan Pembahasanunclassified

unclassified

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

Hafidz

Liliana

2021

RESTI

View full text Add to dashboard Cite

show abstract

“…Pada penelitian sebelumnya mengenai analisis sentimen Korps Brigade Mobil Kepolisian Indonesia berdasarkan posting twitter menggunakan metode SVM Dan NB, menghasilkan nilai akurasi dengan pendekatan SVM 86,96% nilai presisi 86,96%, dan nilai recall 86,96% [7]. Penelitian mengenai perbandingan metode klasifikasi analisis sentimen tokoh politik pada komentar media berita online, menggunakan metode klasifikasi NB dan SVM mendapatkan hasil bahwa, SVM adalah metode terbaik dengan akurasi 78,40% dan AUC 0,850 [8].…”

Section: Pendahuluanunclassified

Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine

Herlinawati

Yuliani

Faizah

et al. 2020

Com, Engine, Sys, Sci

Self Cite

View full text Add to dashboard Cite

Aplikasi zoom cloud meetings yang mulai booming digunakan sekarang ini karena adanya pandemi virus corona, sehingga membuat semua kegiatan dilakukan secara virtual. Zoom cloud meetings merupakan aplikasi yang memiliki berbagai fitur termasuk video & audio conference. Pada penelitian ini penulis menggunakan metode Naïve Bayes dan Support Vector Machine dalam menganalisa label sentimen positif atau negatif pada ulasan para pengguna aplikasi zoom di Google Play Store. Jumlah dataset setelah prepocessing menjadi 1.007 record. Data hampir seimbang dengan label positif sebanyak 546 dan label negatif 461 ulasan. Evaluasi model menggunakan 10 fold cross validation diperoleh nilai akurasi dan nilai AUC dari masing-masing algoritma yaitu untuk NB nilai akurasi = 74,37% dan nilai AUC = 0,659. Sedangkan untuk algoritma SVM nilai akurasi = 81,22% dan nilai AUC = 0,886. Dalam penelitian ini dapat diketahui bahwa tingkat akurasi yang didapatkan algoritma Support Vector Machine (SVM) lebih unggul 6,85% dibandingkan algoritma Naïve Bayes (NB). Kata Kunci— Zoom Cloud Meetings, Google Play Store, Virus Corona, Naïve Bayes, Support Vector Machine. Abstract— Zoom cloud meetings application that began to boom is used today because of the corona virus pandemic, so that all activities are carried out virtually. Zoom cloud meetings is an application that has various features including video & audio conferencing. In this study the authors used the Naïve Bayes method and Support Vector Machine in analyzing positive or negative sentiment labels on the zoom users' reviews on the Google Play Store. The number of datasets after prepocessing is 1,007 records. The data is almost balanced with 546 positive labels and 461 negative labels. Evaluation of the model using 10 fold cross validation obtained accuracy values and AUC values from each algorithm, namely for NB, the accuracy value = 74.37% and the AUC value = 0.659. As for the SVM algorithm the accuracy value = 81.22% and the AUC value = 0.886. In this study it can be seen that the accuracy obtained by the Support Vector Machine (SVM) algorithm is 6.85% superior to the Naïve Bayes (NB) algorithm.

show abstract

“…Preprocessing data is a fundamental step for any Natural Language Processing (NLP) [9]. We used the regular library in Python to remove the random patterns in the tweet.…”

Section: Preprocessing and Storagementioning

confidence: 99%

“…We developed our Socio-Analyzer to determine whether the people reacting on MeToo movement are supporting positively or negatively or neutral. The significant steps in developing SocioAnalyzer is building a sentiment dictionary [1,3,4,9] using Natural Language Analyses with NLTK and classify the test data into one the three categories (positive, neutral, and negative) [2]. Figure 3 shows the process of analyzing the data and categorizing the data.…”

Section: Sentiment Analysismentioning

confidence: 99%

Socio-Analyzer: A Sentiment Analysis Using Social Media Data

Bandi¹,

Fellah²

EPiC Series in Computing

View full text Add to dashboard Cite

The usage of social media is rapidly increasing day by day. The impact of societal changes is bending towards the peoples’ opinions shared on social media. Twitter has re- ceived much attention because of its real-time nature. We investigate recent social changes in MeToo movement by developing Socio-Analyzer. We used our four-phase approach to implement Socio-Analyzer. A total of 393,869 static and stream data is collected from the data world website and analyzed using a classifier. The classifier identify and categorize the data into three categories (positive, neutral, and negative). Our results showed that the maximum peoples’ opinion is neutral. The next higher number of peoples’ opinion is contrary and compared the results with TextBlob. We validate the 765 tweets of weather data and generalize the results to MeToo data. The precision values of Socio-Analyzer and TextBlob are 70.74% and 72.92%, respectively, when considered neutral tweets as positive.

show abstract

Sentiment Analysis of the Indonesian Police Mobile Brigade Corps Based on Twitter Posts Using the SVM And NB Methods

Cited by 13 publications

References 0 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

Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine

Socio-Analyzer: A Sentiment Analysis Using Social Media Data

Contact Info

Product

Resources

About