Sentiment Sentence Extraction Using a Hierarchical Directed Acyclic Graph Structure and a Bootstrap Approach

As the World Wide Web rapidly grows, a huge number of online documents are easily accessible on the Web. We obtain a huge number of review documents that include user’s opinions for products. To classify the opinions is one of the hottest topics in natural language processing. In general, we need a large amount of training data for the classification process. However, construction of training data by hand is costly. The goal of our study is to construct a sentiment tagging tool for particular domains. In this paper, we propose a method of sentiment sentence extraction for the 1st step of the system. For the task, we use a Hierarchical Directed Acyclic Graph (HDAG) structure. We obtained high accuracy with the graph based approach. Furthermore, we apply a bootstrap approach to the sentiment sentence extraction process. The experimental result shows the effectiveness of the method.