Multi-domain Sentiment Classification on Self-constructed Indonesian Dataset

Abstract

Domain-dependence limits the application of a well-trained sentiment classifier based on one domain data in other different domains. To solve this problem, multi-domain sentiment classification has received great attention recently. It aims to construct a domain-specific sentiment classifier at once from datasets of multi-domains. However, research on multi-domain sentiment classification mainly focuses on high-resource languages, and there is no research on Indonesian multi-domain sentiment classification. To fill the gap, we constructed an Indonesian multi-domain dataset, including 489,000 reviews from four domains with three sentiment polarities (positive, neutral, and negative), and proposed an integrated model for Indonesian multi-domain sentiment classification. This model is consisted of lemmatization layer, domain-general module, domain-specific module, and domain classifier module. Based on the Indonesian multi-domain dataset, the model was evaluated and compared with baseline methods commonly used in the sentiment analysis of high-resource languages. The effectiveness of some essential components in the model was also verified. The model achieved an average weighted F1 over four domains with 87.24%, outperforming the baseline methods and demonstrating its effectiveness.