scholarly journals Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier

2022 ◽  
Vol 10 (1) ◽  
Author(s):  
Dianati Duei Putri ◽  
Gigih Forda Nama ◽  
Wahyu Eko Sulistiono
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Accuracy Score ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Abstrak~Dalam penelitian ini akan dilakukan analisis sentimen masyarakat terhadap kinerja Dewan Perwakilan Rakyat (DPR) yang diungkapkan melalui media sosial twitter. Ada beberapa tahap untuk melakukan analisis sentimen , yaitu pengumpulan data (crawling), preporcessing data yang terdiri dari proses cleaning data, tokenization, stop remova dan case folding, splitting data dan klasifikasi data menggunakan metode Naive Bayes Classifier. Penelitian ini menggunakan sebanyak 1546 data tweet. Hasil dari penelitian ini didapatkan bahwa DPR mendapatkan 95 tweet positif dengan polaritas 0.75 atau 75% sentimen positif, 693 tweet netral dengan polaritas 0.79 atau 79% sentimen netral dan 758 tweet negatif dengan polaritas 0.82 atau 82% sentimen negatif dengan accuracy score 0.8 atau 80% berdasarkan data testing sebanyak 20%.Kata kunci : Sentiment Analysis, DPR, Naive Bayes Classifier

scholarly journals Analisis Sentimen Menggunakan Algoritma Naive Bayes Terhadap Review Restoran di Singapura

2020 ◽  
Vol 11 (2) ◽  
pp. 140
Author(s):  
Vynska Amalia Permadi
Keyword(s):  
Sentiment Analysis ◽  
Web Application ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Accuracy Score ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
R Shiny ◽  
Restaurant Reviews

Abstract. Sentiment Analysis Using Naive Bayes Classifier Against Restaurant Reviews in Singapore. Various restaurant options bring up a problem for diners to pick a restaurant to dine in. Thus, visitors usually perceive the restaurant's recommendation or rating in advance to know other diners' opinions about the restaurant. Previous restaurant diners' comments can be presented in sentiment analysis to determine their satisfaction. This research investigates the Naïve Bayes Classifier algorithm's performance in classifying visitors' sentiment based on restaurant diner comments. We will group visitors' comments into two types of sentiment: positive (satisfied) and negative (unsatisfied). The results of the data classification test are analyzed to determine its accuracy. The grouping of visitor satisfaction reviews using the naïve bayes algorithm provides an accuracy score of 73%. Besides, we visualize the research classification results in the browser-based R Shiny web application through word cloud and diagrams.Keywords:restaurant review, sentiment analysis, Naïve Bayes ClassifierAbstrak. Variasi pilihan restoran yang tidak sedikit menjadi salah satu masalah bagi pengunjung ketika ingin memilih restoran. Sehingga, pengunjung biasanya melihat rekomendasi atau penilaian pengunjung lain terhadap restoran tersebut terlebih dahulu untuk mengetahui penilaian pengunjung lain terhadap restoran tersebut. Penilaian atau review pengunjung dapat disajikan dalam analisis sentimen berdasarkan komentar para pengunjung restoran sebelumnya untuk melihat kepuasan pengunjung terhadap restoran tersebut. Penelitian ini dilakukan untuk mengetahui performa algoritma Naïve Bayes Classifier dalam melakukan klasifikasi sentimen berdasarkan komentar pengunjung restoran. Penelitian dilakukan dengan mengklasifikasikan data komentar pengunjung restoran menjadi dua kategori sentimen, yaitu: positif (satisfied) dan negatif (unsatisfied). Hasil pengujian pengklasifikasian data kemudian dianalisis akurasinya. Hasil pengelompokan review kepuasan pengunjung menggunakan algoritma naïve bayes memberikan nilai akurasi sebesar 73%. Visualisasi hasil klasifikasi dari analisis kemudian ditampilkan pada aplikasi berbasis web yaitu R Shiny berupa wordcloud dan diagram. Kata Kunci: penilaian restoran, analisis sentimen, Naïve Bayes Classifier

scholarly journals Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 28-39
Author(s):  
Adri Priadana ◽  
Ahmad Ashril Rizal
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tourism Industry ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Trending Topics ◽  
The Government ◽  
Negative Sentiment

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

2021 ◽  
Author(s):  
Adhitia Erfina ◽  
Moneyta Dholah Rosita Ndk ◽  
Rahmat Hidayat ◽  
Aris Subagja ◽  
Haerul Ramadhan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Application
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