The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Particularly, we propose two effective features to encode phonetic information. Next, we develop a Disambiguate Intonation for Sentiment Analysis (DISA) network using a reinforcement network. It functions as disambiguating intonations for each Chinese character (pinyin). Thus, a precise phonetic representation of Chinese is learned. Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations and outshines the state-of-the-art Chinese character level representations.
This study investigated the development of phonetic awareness, meaning insight into the structure and function of the component of Chinese characters that gives a clue to pronunciation. Participants were 113 Chinese 2nd, 4th, and 6th graders enrolled in a working-class Beijing, China elementary school. The children's task was to represent the pronunciation of 60 semantic phonetic compound characters. As anticipated, both character familiarity and character regularity strongly influenced performance. Children as young as 2nd graders are better able to represent the pronunciation of regular characters than irregular characters or characters with bound phonetics. Phonetic awareness continues to develop over the elementary school years, as is shown by the increasing influence of phonetic regularity on the performance of children in higher grades and the increasing percentage of phonetic-related errors among older children.