  We propose a method named Super Characters for sentiment classification. This
method converts the sentiment classification problem into image classification
problem by projecting texts into images and then applying CNN models for
classification. Text features are extracted automatically from the generated
Super Characters images, hence there is no need of any explicit step of
embedding the words or characters into numerical vector representations.
Experimental results on large social media corpus show that the Super
Characters method consistently outperforms other methods for sentiment
classification and topic classification tasks on ten large social media
datasets of millions of contents in four different languages, including
Chinese, Japanese, Korean and English.
