Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network

Abstract

Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;

Keywords

• Computer Science
• Sentiment Analysis
• Simple RNN
• LSTM
• Bernoulli Naive Bayes
• Energy Independence