Open AccessAPLIKASI SENTIMENT MONITORING UNTUK TWITTER DENGAN ALGORITMA NAIVE-BAYES CLASSIFIER
Author(s) -
Ade Saputra,
Agus Sehatman Saragih
Publication year - 2021
Publication title -
jurnal teknologi informasi (jurusan teknik informatika, fakultas teknik universitas palangka raya)/jurnal teknologi informasi
Language(s) - English
Resource type - Journals
eISSN - 2656-0321
pISSN - 1907-896X
DOI - 10.47111/jti.v15i1.1902
Subject(s) - sentiment analysis , computer science , naive bayes classifier , classifier (uml) , artificial intelligence , social media , data mining , machine learning , natural language processing , support vector machine , world wide web
Every day there are millions of opinion spread across social networks. This is often utilized by various parties to determine the opinion and sentiment of the public towards the product, brand or figures that they hold. Given the abundance of data and opinions, it is not possible to do sentiment analysis manually. In this research, author performs design and implementation of sentiment monitoring application, that could monitor people’s sentiment about a particular keyword, so it is known how the people response to those keywords, whether positive, negative or neutral.From various existing social networks, Twitter is chosen as the source of data that will be monitored. Classification algorithm used here is Naive-Bayes Classifier with Boolean Multinomial model, and feature extraction using unigram word. The training data used is 400,000 data for each type of sentiment, so the total is 1.200.000 data. In the process of classification and training, application will perform stemming to take the root words contained within the tweet. Stemming algorithm used here is Confix Stripping.The methodology of application development that used here is staged delivery. Implementation of application is done using PHP programming language. The result of this research is a sentiment monitoring application that can monitor public sentiment about a particular keyword in a particular time frame. From testing using k-fold cross validation, obtained accuracy rate for sentiment classification amounted to 85%.