# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 2 Materials and Methods
### Methods
#### 2.2.1 Sentiment Analysis of Air Pollution Complaints  
Text sentiment analysis, also known as opinion mining and propensity analysis, aims to detect the polarity of emotions (such as negative or positive emotions) or quantify the intensity of emotions [26; 27]. Currently, there are three common methods for text sentiment analysis, namely \"dictionary-based,\" \"machine-learning,\" and \"deep-learning\" [26]. Both \"machine-learning\" and \"deep-learning\" methods need supervised training. However, sentiment analysis based on a dictionary does not require supervised training, which is the most common and straightforward method. Sentiment analysis based on dictionary refers to calculating sentiment value based on the labeled sentiment dictionary [27]. Therefore, this paper analyzed the public emotion of complaints by constructing the emotional dictionary of air pollution. 1. Construction of the emotional dictionary of air pollution  
Firstly, we extracted verb (verb), noun (noun), adjective (adj), adverb (adv), idiom, and preposition phrase (prep) as candidate emotional words through Chinese word segmentation and part of speech tagging. Then, the labeling criteria shown in Table 2 were formulated to classify air pollution emotion words. The column \"Intensity\" indicates the emotional intensity of the words in the text. These statements in the column \"Description\" are summarized from the data of the complaint platform. We labeled the emotion intensity of emotion words after artificial selection. Combining with the emotional Dictionary of Dalian University of Technology, we removed the duplication and added the modifier dictionary to get the emotional dictionary of air pollution. To better understand the air pollution emotion dictionary constructed in this paper, we selected several examples in the dictionary for illustration, as shown in Table 3.  
In Table 3, there are expressions about its part of speech, emotional intensity, and polarity for each word in the dictionary. The polarity can be divided into neutral (0), positive (+1), and negative (\(-1\)). Since the complaint data are usually negative, the polarity is marked \"\(-1\)\". \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polarity** & **Intensity** & **Description** & **Example** \\ \hline \multirow{6}{*}{-(negative)} & 9 & Beyond endurance, Cannot survive & Pain, cancer \\  & 7 & The atmosphere was foul, muddy & Black smoke, headache, dyspnea \\ \cline{1-1}  & 5 & The dust is rising in clouds & Dust storms, grey \\ \cline{1-1}  & 3 & Has an unpleasant smell & Peculiar smell, stench \\ \cline{1-1}  & 1 & Can feel, but not affected & Pollution, not blue \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The labeling criteria of emotion intensity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotional Words** & **Parts of Pecch** & **Intensity** & **Polarity** \\ \hline Privately & adv & \(1\) & \(-1\) \\ Soot & noun & 3 & \(-1\) \\ Punggent & adj & 5 & \(-1\) \\ Reek to high heaven & idiom & 7 & \(-1\) \\ Killed & verb & 9 & -1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The examples of the air pollution emotion dictionary. 2. Sentiment analysis of air pollution complaints  
Based on the constructed air pollution emotion dictionary, this paper calculated the public complaint emotion value, namely complaint emotion intensity, sentence segmentation, and word segmentation of the complaint text. The specific process is as follows. **Text preprocessing**: We preprocessed the complaint text by sentence segmentation, word segmentation, and removing stop words. **Emotional word matching**: We obtained the words after text preprocessing and matched the constructed air pollution emotion dictionary to get the sentence's emotional words and mark their positions. **Modifier matching**: We matched the words with the modifier dictionary to get the sentence's modifiers and mark their positions. **Complaint emotion intensity**: After matching emotion words and modifiers, we calculated the emotion intensity.