如何在python中显示wordcloud中每个用户发出的推文的正颜色和负颜色类别

我正在尝试使用情感值基于类别和类别频率计数为用户创建wordcloud，如果情感值为正值，则类别应以绿色显示，而负值应以红色显示

|id                                  |sentiment|category|sentiment_count|category_count|rank|
+------------------------------------+---------+--------+---------------+--------------+----+
|276c08e3-29c6-416a-b522-5b1c7ad0a865|positive |Business|9              |9             |1   |
|276c08e3-29c6-416a-b522-5b1c7ad0a865|negative |Business|1              |1             |2   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|negative |World   |10             |10            |1   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|positive |Business|7              |7             |2   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|positive |Sci/Tech|6              |6             |3   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|negative |Business|2              |2             |4   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|negative |Sci/Tech|1              |1             |5   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|positive |Sports  |1              |1             |5   |
|09eb80df-76fd-4d94-8bb1-f4ffb0ece4c6|positive |World   |1              |1             |5   |
|4f11697e-d3f0-47a0-b1f8-aaacf7b720b7|positive |Sports  |12             |12            |1   |
|4f11697e-d3f0-47a0-b1f8-aaacf7b720b7|positive |Sci/Tech|1              |1             |2   |
|f29a2c29-91cf-41d8-a586-453218331710|positive |Sci/Tech|759            |759           |1   |
|f29a2c29-91cf-41d8-a586-453218331710|negative |Business|524            |524           |2   |
|f29a2c29-91cf-41d8-a586-453218331710|positive |Business|508            |508           |3   |
|f29a2c29-91cf-41d8-a586-453218331710|negative |World   |279            |279           |4   |
|f29a2c29-91cf-41d8-a586-453218331710|negative |Sci/Tech|220            |220           |5   |

我试过使用代码。但不确定如何根据情绪对颜色进行分类（消极和积极）

---

@约翰：你有什么解决办法吗？

from wordcloud import WordCloud
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd 
import numpy as np
source1 = 'path_to_csv'
df1 = pd.read_csv(source1)
print(df1)            

uids1 = np.unique(df1['id'])

fig, axes = plt.subplots(nrows=(len(uids)+2)//3, ncols=3, figsize=(20, 8),
                         gridspec_kw={'hspace': 0.05, 'wspace': 0.05, 'left': 0.01, 'right': 0.99, 'top': 0.99, 'bottom': 0.01})
for id, ax in zip(uids, axes.ravel()):
    data = df[df['id'] == id].set_index('category')['category_count'].to_dict()
    wc = WordCloud(width=800, height=400, max_words=200).generate_from_frequencies(data)
    ax.imshow(wc, interpolation='bilinear')
    ax.set_title(f'id = {id}')
    ax.axis('off')
    plt.savefig("/path/wordcloud_twitte.png", format="png")