Semantic Role Labeling for Opinion Target Extraction from Chinese Social Network

Social network is a good resource to collect public opinions considering the diversity and variety in fashion, especially user generated content (UGC). Extracting the opinions from UGC can be the base of commercial policy, so how to extract the opinions correctly is an important problem. However, there are two problems: Are the opinions really talking about the target entities? Or the amount of opinions is enough for network volume analysis? In this study, we combine rule-based method and Semantic Role Labeling (SRL) to detect the opinion target (OTD) from UGC. In SRL task, we design a neural network model to extract the opinion words. Considering gradient vanish problem, we use highway connection to control the percentage of data. To improve the performance of SRL, we use additional features to help model learn the features between verb and other phrases. Finally, we design a rule to filter the result of SRL for OTD task. The experimental results show that our SRL model obtains 71% F1, and our method on OTD task obtains 73% precision which outperforms LTP. This research can be the basis of hot topic prediction for sponsors and helps them to decide marketing strategies