Open AccessSentiment Analysis of Tweets on Prakerja Card using Convolutional Neural Network and Naive Bayes
Author(s) -
Pahlevi Wahyu Hardjita,
_ Nurochman,
Rahmat Hidayat
Publication year - 2022
Publication title -
ijid (international journal on informatics for development)/international journal on informatics for development
Language(s) - English
Resource type - Journals
eISSN - 2549-7448
pISSN - 2252-7834
DOI - 10.14421/ijid.2021.3007
Subject(s) - convolutional neural network , naive bayes classifier , sentiment analysis , computer science , artificial intelligence , deep learning , pattern recognition (psychology) , machine learning , support vector machine
The Indonesian government launched the Prakerja (pre-employment) card in the midst of the COVID-19 pandemic, andthe local citizens have voiced their opinions about this controversial program through social media such as Twitter. People’scomments on it can be useful information, and this research tries to analyze the sentiment regarding the Prakerja Card programusing the Convolutional Neural Network and Naive Bayes methods. The main task in this sentiment analysis is analyzing the dataand then classifying them into one of the following classes: positive, negative or neutral. Naive Bayes is an algorithm that is often usedin sentiment analysis research, and the results have been very good. Convolutional neural network (CNN) is a deep learning algorithmthat uses one or more layers commonly used for pattern recognition and image recognition. Having applied these methods, thisresearch found that the CNN model with the GlobalMaxPooling layer is the best model of the other two CNN models. Sentimentanalysis has the best accuracy of 78.5% on the CNN method, and NBC of 76.2% accuracy. The best accuracy result in K-fold withfive classes is 85.4% on the CNN model with a learning rate optimization of 0.00158. While the average accuracy on NBC only reached75.3%