Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

• Published in: Journal of physics. Conference series Vol. 971; no. 1; pp. 12049 - 12056
• Main Authors: Mariel, Wahyu Calvin Frans, Mariyah, Siti, Pramana, Setia
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2018
• Subjects: Algorithms; Artificial neural networks; Audio data; Classification; Data mining; Deep learning; Feature extraction; Learning theory; Machine learning; Modelling; Neural networks; Pattern recognition; Physics; Sentiment analysis; Support vector machines
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/971/1/012049

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm's ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.