```python import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from snownlp import SnowNLP # 收集整理评论数据，并保存为CSV comments = [...] # 填入收集到的评论数据 df = pd.DataFrame(comments, columns=['comment']) df.to_csv('comments.csv', index=False) # 加载评论数据 df = pd.read_csv('comments.csv') # 对评论进行情感分析 def get_sentiment(text): s = SnowNLP(text) return s.sentiments df['sentiment'] = df['comment'].apply(get_sentiment) # 绘制不同情感类型评论的数量分布饼状图 sentiment_count = df['sentiment'].value_counts() sentiment_labels = ['Positive', 'Neutral', 'Negative'] plt.figure(figsize=(6, 6)) plt.pie(sentiment_count, labels=sentiment_labels, autopct='%1.1f%%') plt.title('Sentiment Distribution of Comments') plt.show() # 按月统计评论量，并绘制曲线图 df['timestamp'] = pd.to_datetime(df['timestamp']) df['month'] = df['timestamp'].dt.to_period('M') monthly_comments = df['month'].value_counts().sort_index() plt.figure(figsize=(10, 6)) sns.lineplot(x=monthly_comments.index, y=monthly_comments.values) plt.title('Monthly Comment Volume') plt.xlabel('Month') plt.ylabel('Number of Comments') plt.xticks(rotation=45) plt.show() # 统计评论获赞数排名前十，并绘制柱状图 top_10_comments = df.nlargest(10, 'likes') plt.figure(figsize=(10, 6)) sns.barplot(x=top_10_comments['comment'], y=top_10_comments['likes']) plt.title('Top 10 Comments by Likes') plt.xlabel('Comment') plt.ylabel('Number of Likes') plt.xticks(rotation=90) plt.show() ``` 注意：以上代码只提供了基本的框架，需要根据实际数据和需求进行相应的调整和修改。 [2024-06-20 10:32:54 | AI写代码神器 | 1057点数解答]