Deep Learning for Social Media Sentiment Analysis.

Fithriasari, Kartika; Jannah, Saidah Zahrotul; Reyhana, Zakya

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre- processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity, and AUC are higher.

SURABAYA (Indonesia); SENTIMENT analysis; SOCIAL learning; DEEP learning; HYACINTHOIDES; CONVOLUTIONAL neural networks; SOCIAL media; USER-generated content

Matematika, 2020, Vol 36, p99

0127-8274

Academic Journal

10.11113/matematika.v36.n2.1226