Classification of multi-lingual tweets, into multi-class model using Naïve Bayes and semi-supervised learning

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

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Analysis and Classification of Danger Level in Android Applications Using Naive Bayes Algorithm

2018 6th International Conference on Information and Communication Technology (ICoICT) ◽

10.1109/icoict.2018.8528733 ◽

2018 ◽

Author(s):

Ridho Alif Utama ◽

Parman Sukarno ◽

Erwid Musthofa Jadied

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Android Applications ◽

Bayes Algorithm ◽

Danger Level

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Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents

Journal of Business and Economic Statistics ◽

10.1080/07350015.2014.903086 ◽

2014 ◽

Vol 32 (3) ◽

pp. 445-456 ◽

Cited By ~ 3

Author(s):

Guoyu Guan ◽

Jianhua Guo ◽

Hansheng Wang

Keyword(s):

Chinese Text ◽

Naive Bayes ◽

Naïve Bayes ◽

Text Documents

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Klasifikasi Metagenom dengan Metode Naïve Bayes Classifier

Jurnal Ilmu Komputer dan Agri-Informatika ◽

10.29244/jika.3.1.9-17 ◽

2017 ◽

Vol 3 (1) ◽

pp. 9

Author(s):

Dian Kartika Utami ◽

Wisnu Ananta Kusuma ◽

Agus Buono

Keyword(s):

Supervised Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier

Studi metagenom merupakan langkah penting pada pengelompokan taksonomi. Pengelompokan pada metagenom dapat dilakukan dengan menggunakan metode binning. Binning diperlukan untuk mengelompokkan contigs yang dimiliki oleh masing-masing kelompok spesies filogenetik. Pada penelitian ini, binning dilakukan dengan menggunakan pendekatan komposisi berdasarkan supervised learning (pembelajaran dengan contoh). Metode supervised learning yang digunakan yaitu Naïve Bayes Classifier. Adapun metode yang digunakan untuk ekstraksi ciri adalah dengan melakukan perhitungan frekuensi k-mer. Klasifikasi pada metagenom dilakukan berdasarkan tingkat takson genus. Dari proses klasifikasi yang dilakukan, akurasi yang diperoleh dengan menggunakan fragmen pendek (400 bp) adalah 49.34 % untuk ekstraksi ciri 3-mer dan 53.95 % untuk ekstrasi ciri 4-mer. Sementara itu, untuk fragmen panjang (10 kbp), akurasi mengalami peningkatan yaitu 82.23 % untuk ekstraksi ciri 3-mer dan 85.89 % untuk esktraski ciri 4-mer. Dari hasil tersebut dapat disimpulkan bahwa akurasi semakin tinggi seiring dengan semakin panjangnya ukuran fragmen. Selain itu, penelitian ini juga menyimpulkan bahwa metode ekstrasi ciri yang memberikan hasil paling maksimal adalah dengan menggunakan ekstraksi ciri 4-mer.<br /><br />Kata Kunci: metagenom, k-mer, Naïve Bayes Classifier, binning, klasifikasi

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Classification of community opinion on the use of the Transjakarta bus based on twitter social network using naïve bayes method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1010/1/012030 ◽

2021 ◽

Vol 1010 ◽

pp. 012030

Author(s):

B. D Meilani ◽

R K Hapsari ◽

I F Novian

Keyword(s):

Social Network ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

JURNAL INFOTEL ◽

10.20895/infotel.v9i4.312 ◽

2017 ◽

Vol 9 (4) ◽

pp. 416 ◽

Cited By ~ 1

Author(s):

Nelly Indriani Widiastuti ◽

Ednawati Rainarli ◽

Kania Evita Dewi

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Support Vector ◽

Good Reputation ◽

Multiclass Support Vector Machine ◽

Simple Logistic ◽

Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

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