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@ -5,36 +5,36 @@ import matplotlib.pyplot as plt
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from PIL import Image
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import numpy as np
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# 1. 读取停用词表
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# 读取停用词表
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def load_stopwords(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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stopwords = set(line.strip() for line in f)
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return stopwords
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# 2. 过滤停用词
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# 过滤停用词
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def remove_stopwords(words_list, stopwords):
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return [word for word in words_list if word not in stopwords and len(word) > 1]
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# 3. 读取Excel文件并提取弹幕内容
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# 读取Excel文件并提取弹幕内容
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file_path = "danmu_data.xlsx"
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df = pd.read_excel(file_path)
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comments = df['danmu'].astype(str)
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text = ' '.join(comments)
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# 4. 使用 jieba 分词
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# 使用 jieba 分词
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words = jieba.cut(text, cut_all=False)
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# 5. 加载停用词表
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# 加载停用词表
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stopwords_file = "D://edge//stop.txt" # 替换为实际路径
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stopwords = load_stopwords(stopwords_file)
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# 6. 去除停用词
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# 去除停用词
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filtered_words = remove_stopwords(words, stopwords)
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# 7. 将过滤后的词汇重新拼接为一个字符串
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# 将过滤后的词汇重新拼接为一个字符串
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words_list = ' '.join(filtered_words)
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# 8. 加载形状图片并生成词云
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# 加载形状图片并生成词云
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mask = np.array(Image.open("D://edge//kk.png"))
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wordcloud = WordCloud(
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@ -47,10 +47,10 @@ wordcloud = WordCloud(
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height=600
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).generate(words_list)
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# 9. 显示词云图
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# 显示词云图
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis("off")
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plt.show()
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# 10. 保存词云图
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# 保存词云图
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wordcloud.to_file("filtered_wordcloud.png")
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