Grammatical phrase-level opinion target extraction on Chinese microblog messages

Abstract

Microblog is one of the most widely used web applications. Weibo, which is a microblog service in China, produces plenty of opinionated messages every second. Sentiment analysis on Chinese Weibo impacts many aspects of business and politics. In this work, we attempt to address the opinion target extraction, which is one of the most important aspects of sentiment analysis. We propose a unified approach that concentrates on phrase-level target extraction. We assume that a target is represented as a subgraph of the sentence's dependency tree and define the grammatical relations that point to the target word as TARRELs. We conduct the extraction by classifying grammatical relations with a cost-sensitive classifier that enhances performance of unbalanced data and figuring out the target subgraph by connecting and recovering TAR-RELs. Then we prune the noisy targets by empirically summarized rules. The evaluation results indicate that our approach is effective to the phrase-level target extraction on Chinese microblog messages. © Springer-Verlag Berlin Heidelberg 2013.