The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, emotions were displayed at the highest levels and the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of and increased simultaneously. After the initial victory in pandemic prevention and control, the expression of and reached a peak, while the increase of was the most prominent. The fine-grained sentiment lexicon, which includes a category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.