Deep Learning for Social Media Sentiment Analysis

Authors

  • Kartika Fithriasari Institut Teknologi Sepuluh Nopember
  • Saidah Zahrotul Jannah Institut Teknologi Sepuluh Nopember
  • Zakya Reyhana Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.11113/matematika.v36.n2.1226

Abstract

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.

Author Biographies

Kartika Fithriasari, Institut Teknologi Sepuluh Nopember

Department of Statistics, Institut Teknologi Sepuluh Nopember

Saidah Zahrotul Jannah, Institut Teknologi Sepuluh Nopember

Department of Statistics, Institut Teknologi Sepuluh Nopember

Zakya Reyhana, Institut Teknologi Sepuluh Nopember

Department of Statistics, Institut Teknologi Sepuluh Nopember

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Published

2020-08-01

How to Cite

Fithriasari, K., Jannah, S. Z., & Reyhana, Z. (2020). Deep Learning for Social Media Sentiment Analysis. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 36(2), 99–111. https://doi.org/10.11113/matematika.v36.n2.1226

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Articles