Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa

Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa

CNN, Glove, RoBERTa, Sentiment Analysis, Twitter

Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time
conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which
makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion,
whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and
emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia,
sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse
Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Oversampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags
related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows
the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus
tweet with an increase 10.1%

Diaz Tiyasya Putra, Erwin Budi Setiawan

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

June 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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