Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System

Sentiment analysis in social media has attracted significant attention. Although researchers have proposed many methods, a single method is hard to meet requirement in industrial applications. In this paper, based on massive data of Tencent and industrial practice, we present a multilayered analysis system (MAS) on social media. The system is composed of three sub-systems, including topic correlation calculation, topic-related sentence recognition and sentence polarity classification. Each sub-system is composed of several simple models. Also, we have set up a closed-loop feature mining and model updating system, which will continuously promote performance of MAS. In addition, this offline system requires very little intervention. The system, including online and offline parts, has been applied in several practical projects and obtained the best results in the evaluation of task 2 of SIGHAN-8.