CNN-Based Sequence Labeling for Fine-Grained Opinion Mining of Microblogs

Abstract

Opinion mining on microblogs is of significance because microblogging websites have attracted many users to share their experiences and express their opinions on a variety of topics. However, conventional opinion mining methods focus mainly on sentiment of texts and ignore opinion target. This paper focuses on a fine-grained opinion mining task that jointly extract opinion target and corresponding sentiment by sequence labeling. We propose a convolutional neural network (CNN)-based sequence labeling method and apply it to fine-grained opinion mining of microblogs. We empirically evaluated neural networks with different filter length and depth and analyzed the boundary of contextual feature extraction for opinion mining of microblogs. The experimental results demonstrate that the proposed CNN-based methods are better than RNN-based methods in both effectiveness and efficiency.