Creating a Fine-Grained Corpus for Chinese Sentiment Analysis

Abstract

Writing comments on products or news has become a popular activity in social media. The amount of opinionated text available online has been growing rapidly, increasing the need for techniques that can analyze opinions expressed in such text so that reviews can be easily absorbed by users. To date, most techniques depend on annotated corpora. However, existing corpora are almost sentence-level works that ignore important global sentiment information in other sentences. Given the rise of advanced applications, more fine-grained corpora are needed, even at the sentence level. The authors aim to create a fine-grained corpus for Chinese sentiment analysis, and more importantly, explore new sentiment analysis tasks by analyzing the annotated corpus. The proposed fine-grained annotation scheme not only introduces cross-sentence and global sentiment information (such as "target entity"') but also includes new sentence-level elements (such as "implicit aspect"). Based on this scheme, this corpus can provide a more fine-grained platform for researchers to study algorithms for advanced applications. In addition, an in-depth analysis on the annotated corpus is made and several important but ignored tasks, such as the target-aspect pair extraction task, are explored, which can give useful hints about future directions.