  In the sentiment analysis task, predicting the sentiment tendency of a
sentence is an important branch. Previous research focused more on sentiment
analysis in English, for example, analyzing the sentiment tendency of sentences
based on Valence, Arousal, Dominance of sentences. the emotional tendency is
different between the two languages. For example, the sentence order between
Chinese and English may present different emotions. This paper tried a method
that builds a domain-specific lexicon. In this way, the model can classify
Chinese words with emotional tendency. In this approach, based on the [13], an
ultra-dense space embedding table is trained through word embedding of Chinese
TikTok review and emotional lexicon sources(seed words). The result of the
model is a domain-specific lexicon, which presents the emotional tendency of
words. I collected Chinese TikTok comments as training data. By comparing The
training results with the PCA method to evaluate the performance of the model
in Chinese sentiment classification, the results show that the model has done
well in Chinese. The source code has released on
github:https://github.com/h2222/douyin_comment_dataset
