Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU

Abstract

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.

Keywords

• Annotation
• Deep Learning
• GRU
• LSTM
• Semi-Supervised Learning
• Word2Vec