Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes

Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

“…Rapid miner is moreover used to help in making analysis sentiments for comparison by using three different classifications within the dataset. The results of this research are the highest accuracy is the Naïve Bayes algorithm of 77% [21].…”

Analysis sentiment about islamophobia when Christchurch attack on social media

“…There are several studies on Islamophobic and The Use of Twitter's Data, as follows: "Detecting weak and strong Islamophobic hate speech on social media" build multi-class classifiers that distinguish between non-Islamophobic, weak Islamophobic and strong Islamophobic content [10], "Predicting online Islamophobic behavior after #ParisAttacks" in this research collected tweets related to the Paris attacks within 50 hours after the event [11], "Election result prediction using Twitter sentiment analysis" the training dataset, for labeling, using valence aware dictionary and sentiment reasoner (VADER) Next use two algorithms, multinomial Naïve Bayes and SVM [12], "Sentiment analysis about e-commerce from tweet using decision tree , k-nearest neighbor, and Naïve Bayes" use rapidminer to make sentiment analysis by comparing the Decision Tree, K-NN, and Naïve Bayes Classifier and using 10-Fold Cross validation to evaluate the performance of the machine learning model and get the highest results from Naïve Bayes [13], "Analisis Sentimen Pada Review Konsumen Menggunakan Metode Naïve Bayes Dengan Seleksi Fitur Chi Square Untuk Rekomendasi Lokasi Makanan Tradisional" using feature selection the chi-square and classification process using the Naïve Bayes method [14], "An evaluation of SVM and Naïve Bayes with SMOTE on sentiment analysis data set" determine the factors involving the classification of SVM and Naïve Bayes in sentiment classification of problems using SMOTE in the dataset, also comparing the use of 10-fold cross validation with 70:30 split in the test [15].…”

Uma Abordagem de Classificação de Sentimentos em Revisões de Livros em Português Brasileiro Usando Diferentes Métodos de Extração de Características

“…AÁrvore de Decisão (Decision Tree -DT) [Prasad et al 2015, Bilal et al 2016, Bayhaqy et al 2018]é uma estrutura de fluxograma semelhante a umaárvore. A estruturaéútilizada para aplicar um conjunto de regras, onde cada nó internoé responsável por testar um atributo, cada ramificação representa os resultados do teste de determinado nó e cada folha indica o rótulo da classe.…”