Python之酒店评论分词词性标注TF-IDF词频统计词云

Posted 2020-12-13 雨后观山色

1.jieba分词与词性标注

思路：

(1)利用pandas读取csv文件中的酒店客户评论，并创建3个新列用来存放分词结果、词性标注结果、分词+词性标注结果

(2)利用jieba分词工具的posseg包，同时实现分词与词性标注

(3)利用停用词表对分词结果进行过滤

(4)将分词结果以20000条为单位写入txt文档中，便于后续的词频统计以词云的制作

(5)将最终的分词结果与词性标注结果存储到csv文件中

# coding:utf-8
import pandas as pd
import jieba.posseg as pseg
import jieba
import time
from jieba.analyse import *
df=pd.read_csv(\'csvfiles/hotelreviews_after_filter_utf.csv\',header=None) #hotelreviews50_1.csv文件与.py文件在同一级目录下
#在读数之后自定义标题
columns_name=[\'mysql_id\',\'hotelname\',\'customername\',\'reviewtime\',\'checktime\',\'reviews\',\'scores\',\'type\',\'room\',\'useful\',\'likenumber\']
df.columns=columns_name
df[\'review_split\']=\'new\' #创建分词结果列：review_split
df[\'review_pos\']=\'new\' #创建词性标注列：review_pos
df[\'review_split_pos\']=\'new\' #创建分词结果/词性标注列：review_split_pos

#   调用jieba分词包进行分词
def jieba_cut(review):
    review_dict = dict(pseg.cut(review))
    return review_dict

# 创建停用词列表
def stopwordslist(stopwords_path):
    stopwords = [line.strip() for line in open(stopwords_path,encoding=\'UTF-8\').readlines()]
    return stopwords

# 获取分词结果、词性标注结果、分词结果/分词标注结果的字符串
def get_fenciresult_cixin(review_dict_afterfilter):
    keys = list(review_dict_afterfilter.keys()) #获取字典中的key
    values = list(review_dict_afterfilter.values())
    review_split="/".join(keys)
    review_pos="/".join(values)
    review_split_pos_list = []
    for j in range(0,len(keys)):
        review_split_pos_list.append(keys[j]+"/"+values[j])
    review_split_pos=",".join(review_split_pos_list)
    return review_split,review_pos,review_split_pos


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，对比很满意，屋里有消毒柜可以消毒杯子，每天都有送两个苹果。三楼还有自助洗衣，住客是免费的，一切都干干净净，服务也很贴心，在这寒冷的冬天，住这里很温暖很温馨"
#分词与词性标注
def fenci_and_pos(review):
    #01 调用jieba的pseg同时进行分词与词性标注，返回一个字典 d = {key1 : value1, key2 : value2 }
    review_dict= jieba_cut(review)
    # print(review_dict)
    # 02 停用词过滤
    review_dict_afterfilter = {}
    for key, value in review_dict.items():
        if key not in stopwordslist:
            review_dict_afterfilter[key] = value
        else:
            pass
    # print(review_dict_afterfilter)
    #03 获取分词结果、词性标注结果、分词+词性结果
    review_split, review_pos,review_split_pos = get_fenciresult_cixin(review_dict_afterfilter)
    return review_split,review_pos,review_split_pos

def fenci_pos_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs

# fenci_and_pos(review)
# jieba.load_userdict(\'stopwords_txt/user_dict.txt\') #使用用户自定义的词典

start_time = time.time()
review_count=0
txt_id = 1
for index,row in df.iterrows():
    reviews=row[\'reviews\']
    review_split, review_pos, review_split_pos=fenci_and_pos(reviews)
    # print(review_split)
    # print(review_pos)
    # print(review_split_pos)
    review_mysql_id=row[\'mysql_id\']
    print(review_mysql_id)  #输出当前分词的评论ID

    df.loc[index,\'review_split\']=review_split
    df.loc[index,\'review_pos\']=review_pos
    df.loc[index,\'review_split_pos\']=review_split_pos
    #review_split 将分词结果逐行写入txt文档中
    if review_count<20000:
        review_count+=1  #计数+1
        review_split_txt_path = \'split_result_txt/split_txt_\' + str(txt_id) + \'.txt\'
        f = open(review_split_txt_path, \'a\', encoding=\'utf-8\')
        f.write(\'\\n\' + review_split)
        f.close()
    else:
        txt_id+=1
        review_count=0
        review_split_txt_path = \'split_result_txt/split_txt_\' + str(txt_id) + \'.txt\'
        f = open(review_split_txt_path, \'a\', encoding=\'utf-8\')
        f.write(\'\\n\' + review_split)
        f.close()

df.to_csv(\'csvfiles/hotelreviews_fenci_pos.csv\', header=None, index=False)  # header=None指不把列号写入csv当中
# 计算分词与词性标注所用时间
end_time = time.time()
fenci_mins, fenci_secs = fenci_pos_time(start_time, end_time)
print(f\'Fenci Time: {fenci_mins}m {fenci_secs}s\')
print("hotelreviews_fenci_pos.csv文件分词与词性标注已完成")

2.词频统计

#词频统计函数
def wordfreqcount(review_split_txt_path):
    wordfreq = {}  # 词频字典
    f = open(review_split_txt_path, \'r\', encoding=\'utf-8\') #打开分词结果的txt文件
    review_split = ""
    #逐行读取文件，将读取的字符串用/切分，遍历切分结果，统计词频
    for line in f.readlines():
        review_words = line.split("/")
        keys = list(wordfreq.keys())
        for word in review_words:
            if word in keys:
                wordfreq[word] = wordfreq[word] + 1
            else:
                wordfreq[word] = 1

    word_freq_list = list(wordfreq.items())
    word_freq_list.sort(key=lambda x: x[1], reverse=True)
    return word_freq_list

#设置分词结果保存的txt路径
txt_id = 1
review_split_txt_path = \'split_result_txt/split_txt_\' + str(txt_id) + \'.txt\'
word_freq_list=wordfreqcount(review_split_txt_path)
#输出词频前10的词汇及其出现频次
for i in range(10):
    print(word_freq_list[i])

3.词云制作

首先利用conda安装wordcloud

conda install -c conda-forge wordcloud

最简单的入门案例：

import wordcloud

# 构建词云对象w，设置词云图片宽、高、字体、背景颜色等参数
w = wordcloud.WordCloud(width=1000,height=700,background_color=\'white\',font_path=\'msyh.ttc\')

# 调用词云对象的generate方法，将文本传入
w.generate(\'从明天起，做一个幸福的人。喂马、劈柴，周游世界。从明天起，关心粮食和蔬菜。我有一所房子，面朝大海，春暖花开\')

# 将生成的词云保存为output2-poem.png图片文件，保存到当前文件夹中
w.to_file(\'output2-poem.png\')

效果图：

我的词云案例：

import jieba
import wordcloud

# 导入imageio库中的imread函数，并用这个函数读取本地图片，作为词云形状图片
import imageio
mk = imageio.imread("pic/qiqiu2.png")
# 构建并配置词云对象w
w = wordcloud.WordCloud(
                        max_words=200,  # 词云显示的最大词数
                        background_color=\'white\',
                        mask=mk,
                        font_path=\'msyh.ttc\', #字体路径，文件中没有(应该是无效设置)
                        )

#设置分词结果保存的txt路径
txt_id = 1
review_split_txt_path = \'split_result_txt/split_txt_\' + str(txt_id) + \'.txt\'
f = open(review_split_txt_path, \'r\', encoding=\'utf-8\')
string=""
for line in f.readlines():
    string+=line
print(string)

# 将string变量传入w的generate()方法，给词云输入文字
w.generate(string)
# 将词云图片导出到当前文件夹
w.to_file(\'output5-tongji.png\')

效果图：

参考文献：https://www.cnblogs.com/wkfvawl/p/11585986.html

4.TF-IDF 关键词提取

import jieba
txt_id=1
review_split_txt_path=\'split_result_txt/split_txt_\'+str(txt_id)+\'.txt\'
f = open(review_split_txt_path, \'r\',encoding=\'utf-8\')
review_split=""
for line in f.readlines():
    review_split+=line
print("review_split:"+review_split)

# test_reviews="刚刚才离开酒店，这是一次非常愉快满意住宿体验。"
# review_split, review_pos, review_split_pos=fenci_and_pos(test_reviews)
# print(review_split)
keywords = jieba.analyse.extract_tags(review_split,topK = 10, withWeight = True)
print(\'【TF-IDF提取的关键词列表：】\')
print(keywords)   #采用默认idf文件提取的关键词

 参考文献：https://blog.csdn.net/asialee_bird/article/details/81486700    TF-IDF算法介绍及实现