In recent years,many researchers have confirmed that deep learning based multi feature fusion sentiment analysis methods aremore capable of mining emotional information in texts than pure deep learning methods,with emotional word features being one of themost important features. At present,although there are a small number of emotional lexicon?in Tibetan,they are basically not publicly available. If you want to use Tibetan emotional lexicon resources,you can only build them yourself. Studying the current construction status of Tibetan emotion lexicon can provide assistance for the subsequent construction of Tibetan emotion lexicon. In order tounderstand the vocabulary classification methods, commonly used lexicon construction methods,and the current research status of the vocabulary and composition of existing Tibetan emotional lexicon,we analyze the literature related to the construction of Tibetan emotionallexicon in the past 10 years ( mainly CHKI) through comparative and statistical methods, and summarize the research status of theconstruction of Tibetan emotional lexicon. Through research,it has been found that the classification methods for emotional words mainlyinclude 7 categories and 21 subcategories, 12 categories and 20 subcategories, 2 categories and 18 subcategories. The constructionmethods of Tibetan emotional lexicon include lexicon matching,machine translation,SO-PMI expansion,similarity expansion based onword2vec or BERT,etc. The vocabulary of existing Tibetan emotional lexicon is roughly distributed between 5 000 and 28 000,close tothe level of Chinese emotional lexicon. The vocabulary composition mainly includes emotional words,degree adverbs,negative words,double negative words, emoticons, etc. We hope to provide reference for researchers and those who are building Tibetan emotional lexicon.