Japanese Opinion Extraction System for Japanese Newspapers Using Machine -Learning Method

We constructed a Japanese opinion extraction system for Japanese newspaper articles using a machinelearning method for the system. We used opinionannotated articles as learning data for the machinelearning method. The system extracts opinionated sentences from newspaper articles, and specifies opinion holders and opinion polarities of the extracted sentences. The system also evaluates whether or not the sentences of the articles are relevant to the given topic. We conducted experiments using the NTCIR-6 opinion extraction subtask data collection and obtained the following accuracy rates using a lenient gold standard: opinion extraction, 42.88%; opinion holder extraction, 14.31%; polarity decision, 19.90%; and relevance evaluation, 63.15%.