Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis

Abstract

Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is ​​made to evaluate the level of accuracy.

Perbandingan Algoritma Naïve Bayes Dan K-Nearest Neighbor Pada Analisis Sentimen Ulasan Aplikasi Mypertamina

“…Algoritma Naïve Bayes yang akan digunakan pada penelitian ini ialah algoritma Multinomial Naïve Bayes. Hal ini didasarkan pada penelitian yang dilakukan oleh [20]. Penelitian tersebut menghasilkan kesimpulan yaitu tingkat akurasi yang paling tinggi ialah algoritma Multinomial Naïve Bayes dengan tingkat akurasi 63,74% .…”

Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer

“…These studies have delved into various applications of sentiment analysis, ranging from analyzing sentiments toward COVID-19 vaccines [23], traffic risk management [33], hotel reviews [34], public trust in government policies during the pandemic [35], to sentiments related to the COVID-19 booster vaccine [36]. Moreover, sentiment analysis has been conducted on a wide array of subjects, including sentiments towards airlines [37], academic articles [38], Indonesian general analysis datasets [39], Bali tourism during the pandemic [40], internet service providers [41], work from home policies [42], and technology utilization by local governments [43]. These studies have used algorithms like Naïve Bayes and Support Vector Machine (SVM) to compare public responses and categorize sentiments into positive, negative, and neutral classes [44].…”

Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes

“…The process in implementing this system will use the term frequency to find the pattern of the algorithm by performing the Multinomial Naïve Bayes algorithm calculation flow according to Figure 2 so that the expected results to compare document predictions are more accurate by using a dataset that has been prepared in the form of a classified document. The Naïve Bayes Classifier algorithm is an algorithm used to find the highest probability value and then classify the test data in the most appropriate category [35]- [37]. In this study, the test data are documents that come from social media.…”