In recent years, social networking platforms such as Sina Weibo and Twitter have gradually become one of the main carriers for reflecting social public opinion, providing a convenient platform for netizens to express their opinions and emotions.Public opinion monitoring based on social network big data has become a new research hotspot.People’s emotions monitoring using social network big data in various countries is helpful to directly grasp people’s emotional tendencies in international relations, and has a great impact on the diplomacy, foreign trade, and other aspects.Based on this, a public sentiment monitoring system for Chinese and Japanese data was proposed, which could analyze the emotional tendencies contained in Chinese and Japanese data on social platforms such as Sina Weibo and Twitter simultaneously, and displayed them to users in a visual form.In the aspect of sentiment analysis algorithm, based on the BERT model and combined with the self-expanding Chinese and Japanese sentiment lexicon, a new sentiment analysis model, EmoBERT, was proposed.The experimental results show that, compared with the original BERT model, the EmoBERT has achieved good results on both Chinese sentiment classification tasks and Japanese sentiment classification tasks.Among them, EmoBERT-C increases the accuracy of Chinese BERT from 89.68% to 92.15%, and EmoBERT-J increases the accuracy of Japanese BERT model from 74.73% to 78.26%.
