## 3 Research Methods
### Sentiment Analysis  
Sentiment analysis, a critical technology in text mining, involves analyzing, processing, summarizing, and reasoning through texts that contain subjective and emotional content. This technique enables the automatic extraction of positions, viewpoints, opinions, emotions, and preferences from textual data [38]. By utilizing sentiment analysis, natural language texts containing subjective descriptions can be automatically analyzed to determine their positive or negative emotional tendencies, thereby providing corresponding analytical results. With the ubiquity of social media, sentiment analysis has become an indispensable tool for understanding the emotions of topic initiators and gauging overall public sentiment [30]. Therefore, we employed sentiment analysis to ascertain the emotional tendencies of the Chinese public towards common prosperity, specifically focusing on identifying the primary sources of public dissatisfaction. This analysis is crucial, as it will undoubtedly influence China's policy direction.  
In this study, we employed a sentiment word matching algorithm to analyze text sentiment. The procedure was as follows:  
Firstly, text preparation and segmentation. Initially, preprocessed posts were formatted in Excel, with each cell containing one post, referred to as a \"text body\". These text bodies were divided into sentences using various punctuation marks, including Chinese and English period marks, half-width and full-width question marks, exclamation marks, semicolons, vertical bars, and tab characters. Within these sentences, sentiment words, negation words, and degree words were identified and ordered as they appeared.  
Secondly, weight assignment and sentiment calculation. Using a sentiment dictionary from GooSeeker11.5.1 software, enhanced with a custom-built sentiment dictionary, weights were assigned to sentiment words, negation words, and degree words. Sentiment words received integer weights: positive numbers indicated positive sentiments (range: 0 < n < 10) and negative numbers reflected negative sentiments (range: \(-10\leq\text{n}<0\)), where a higher absolute value indicates stronger sentiment intensity. Negation words, which invert the sentiment of adjacent words, were assigned a weight of \(-1\). Degree words, which can amplify the effect of sentiment words, vary in value from 0 < n < 2. The specific weights for common degree words are detailed in Table 3.  
Thirdly, sentiment calculation. Starting from the rightmost word in the sequence, if a word is identified as a negation or degree word, a search was initiated for an adjacent sentiment word to the left. If no sentiment word was found on the left, the search continues to the right. When a sentiment word was located, its weight was adjusted by multiplying it with the weight of the negation or degree word. This process was repeated until all words were evaluated. Subsequently, the weights of all sentiment words were summed to compute the overall sentiment score of the sentence. Posts were classified based on the sentiment score as follows: positive if greater than zero, negative if less than zero, and neutral if equal to zero.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Degree Word** & **Weight** & **Degree Word** & **Weight** \\ \hline Extreme & 3 & Very & 2 \\ Super & 3 & Quite & 2 \\ Greatly & 2 & Slightly & 1 \\ More & 2 & Somewhat & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Degree word weights.