In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new “optimistic humorous type” and “pessimistic humorous type” added to standard “positive” and “negative” sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network.

在本文中，我们介绍了HEMOS（^ h umor- EMO霁小号基于lang的系统）使用深度学习方法对中文进行细粒度的情感分类。我们调查了认识幽默，象形文字和语对社交媒体情感处理任务的影响的重要性。第一步，我们收集了576种常用的互联网语作为expression语词典；然后，我们将109个微博表情符号转换为文字特征，从而创建了中文表情词典。下一步，通过执行两个极性注释，并将新的“乐观幽默类型”和“悲观幽默类型”添加到标准“积极”和“消极”情感类别中，我们将这两种词汇都应用于基于注意力的双向长短术语记忆递归神经网络（AttBiLSTM），并测试了其在尺寸过小的标记数据上的性能。