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

Building domain specific lexicon based on TikTok comment dataset