import jieba def get_stop_words(filepath) -> list: return open(filepath, 'r', encoding='utf-8').readlines()[0].split(',') # 对句子进行分词 def seg_sentence(sentence, stop_words): sentence_seged = jieba.cut(sentence.strip()) stopwords = stop_words # 这里加载停用词 outstr = ''...
以下是一个简单示例,演示如何使用nltk库的停词表去除文本中的停词: fromnltk.corpusimportstopwordsfromnltk.tokenizeimportword_tokenize stop_words=set(stopwords.words('english'))text="This is an example sentence demonstrating how to remove stopwords."tokens=word_tokenize(text)filtered_text=[wordforwordint...
extend(custom_stopwords) # 将自定义停用词添加到列表中 return stopwords # 加载停用词并添加自定义停用词 custom_stopwords = ['中', '年', '类'] stopwords = stopwordslist('哈工大中文停用词.txt', custom_stopwords) import jieba # 定义数据清洗函数 def data_cleaning(content_list): # 初始化清洗...
# 导入模块fromwordcloudimportWordCloud# 文本数据text='he speak you most bueatiful time Is he first meeting you'# 准备禁用词,需要为set类型stopwords=set(['he','is'])# 设置参数,创建WordCloud对象wc=WordCloud(width=200,# 设置宽为400pxheight=150,# 设置高为300pxbackground_color='white',# 设置...
readlines()] stopwords.append(' ') # 自定义添加停用词 return stopwords def movestopwords(sentence): stopwords = stopwordslist() # 加载停用词的路径 santi_words =[x for x in sentence if len(x) >1 and x not in stopwords] return ' '.join(santi_words) data_cut = jieba.lcut(str(txt))...
stopwords = stopwordslist('filename') # 这里加载停用词的路径 outstr = '' for word in sentence_seged: if word not in stopwords: if word != '\t': outstr += word outstr += " " return outstr inputs = open('filename', 'r') #加载要处理的文件的路径 ...
stopwords.head() 接下里我们只要适当更改分词的代码即可在分词的时候去掉停用词: # 转化词列表 stop_list = stopwords['stopword'].tolist() #去除停用词 data['cut'] = data['comment'].apply(lambda x : [i for i in jieba.cut(x) if i not in stop_list]) data.head() ...
stopwordslist=stopwordslist("stopwords_txt/total_stopwords_after_filter.txt")#review="刚刚才离开酒店,这是一次非常愉快满意住宿体验。酒店地理位置对游客来说相当好,离西湖不行不到十分钟,离地铁口就几百米,周围是繁华商业中心,吃饭非常方便。酒店外观虽然有些年头,但里面装修一点不过时,我是一个对卫生要求高的...
#引入停用词表defstopwordslist(filepath): stopwords= [line.strip()forlineinopen(filepath,'r', encoding='utf-8').readlines()]returnstopwords 再进行jieba分词,去停使用的是哈工大停用词表 #用lcut使得分词后为一个lists_list =jieba.lcut(content_str) ...
defget_custom_stopwords(stop_words_file):withopen(stop_words_file)asf:stopwords=f.read()stopwords_list=stopwords.split('\n')custom_stopwords_list=[iforiinstopwords_list]returncustom_stopwords_list stop_words_file='哈工大停用词表.txt'stopwords=get_custom_stopwords(stop_words_file)vect=CountVectoriz...