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import jieba
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import re
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from gensim import corpora,models,similarities
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import wordcloud
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import pandas as pd
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import imageio
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class processor():
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def __init__(self,data_path,train_path,userdict,exl_path,wordcloud_path):
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#保存数据的路径
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self.data_path = data_path
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#参照相似度的文件的路径
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self.train_path = train_path
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#更新词库路径
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self.userdict = userdict
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#保存的excel文件路径
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self.exl_path = exl_path
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#词云的文件路径
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self.wordcloud_path = wordcloud_path
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#停用词设置
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self.stop_words = ['什么','亚洲','官方','俄罗斯']
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#本文本词袋词袋
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self.dictionary = None
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def read_file(self,path):
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'''
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读取文件，并存储到列表中
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'''
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with open(path,'r',encoding = 'utf-8') as f:
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lines = f.readlines()
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return lines
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def save_file(self,word_list):
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path = './temp.txt'
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with open(path,'w+',encoding='utf-8') as f:
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f.write(''.join(word_list))
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return path
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def analyse_vector_tfidf(self):
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'''
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抽取与AI相关性
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较为强烈的弹幕
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'''
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#加载词库
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jieba.load_userdict(self.userdict)
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#将数据的全部分词保存在all_doc
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all_lines = self.read_file(self.data_path)
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all_doc = []
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#与AI应用相关的文本数据
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test_lines = self.read_file(self.train_path)
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test_doc = []
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#开始分词
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#对需要分析的数据集开始分词
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for line in all_lines:
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#利用正则表达式去除中文标点,空的话相关度为零
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line = re.sub('[^\w\s]','',line)
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token = [word for word in jieba.lcut(line.strip()) if word not in self.stop_words and len(word)>1]
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all_doc.append(token)
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#对AI应用相关的文本进行分词
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for line in test_lines:
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line = re.sub('[^\w\s]','',line)
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token = [word for word in jieba.lcut(line.strip()) if word not in self.stop_words and len(word)>1]
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if len(test_doc)==0:
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test_doc = token
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else:
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test_doc += token
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#制作词袋
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self.dictionary = corpora.Dictionary(all_doc)
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#制作语料库,即具有词频的词袋模型（词语ID号，词语频率）
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corpus = [self.dictionary.doc2bow(doc) for doc in all_doc]
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test_doc_vec = [self.dictionary.doc2bow(test_doc)]
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#每个词语的tfidf值
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tfidf = models.TfidfModel(corpus)
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#相似度Sim算法
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sparse_matrix = similarities.SparseMatrixSimilarity(tfidf[corpus],num_features=len(self.dictionary.keys()))
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sim = sparse_matrix[tfidf[test_doc_vec]][0]
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sim = sorted(enumerate(sim),key= lambda item: item[1],reverse=True)
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# print(sim)
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#提取在规定相关度之内的弹幕
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key_words = []
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for i in range(len(all_lines)):
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if sim[i][1]>=0.135:
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key_words.append(all_lines[sim[i][0]])
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else:
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break
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self.save_file(key_words)
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def draw_wordcloud(self):
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#制作词频表
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key_word_dict = {}
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key_words = self.read_file('temp.txt')
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key_words_list = []
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for line in key_words:
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#利用正则表达式去除中文标点,空的话相关度为零
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line = re.sub('[^\w\s]','',line)
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token = [word for word in jieba.lcut(line.strip()) if word not in self.stop_words and len(word)>1]
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if len(key_words_list)==0:
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key_words_list = token
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else:
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key_words_list += token
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key_words_vec = self.dictionary.doc2bow(key_words_list)
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# print(key_words_vec)
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for tup in key_words_vec:
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key_word_dict[self.dictionary[tup[0]]] = tup[1]
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key_word_dict = sorted(key_word_dict.items(),key=lambda x:x[1],reverse=True)
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print(key_word_dict)
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# list = []
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# for k,v in key_words_list:
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# list.append(k+' '+v)
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self.save_file([f'{items[0]} {items[1]} ' for items in key_word_dict])
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#保存Excel表格
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self.save_to_excel(key_word_dict)
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#画词云
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#要有中文字体的路径不然显示不出中文
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w = wordcloud.WordCloud(background_color='white',mask = imageio.imread('./AI.png'),font_path= './24华康POP2体.TTC')
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w.generate(str(self.read_file('./temp.txt')))
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w.to_file(self.wordcloud_path)
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def save_to_excel(self,key_word_dict):
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execl_data = pd.DataFrame(key_word_dict)
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execl_data.to_excel(self.exl_path)
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if __name__ == '__main__':
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# analyse_vector_tfidf()
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path = './关于b站视频2024年巴黎运动会AI应用的弹幕.txt'
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test_path = './巴黎奥运会AI文本库.txt'
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userdict_path = './AI专有名词.txt'
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exl_path = './b站视频2024年巴黎运动会AI应用的弹幕的统计表格.xlsx'
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wordcloud_path = './AImap.png'
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process = processor(path,test_path,userdict_path,exl_path,wordcloud_path)
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process.analyse_vector_tfidf()
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process.draw_wordcloud()
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