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python 爬取京東指定商品評(píng)論并進(jìn)行情感分析

更新時(shí)間：2021年05月27日 17:16:13 作者：DA1YAYUAN

本文主要講述了利用Python網(wǎng)絡(luò)爬蟲(chóng)對(duì)指定京東商城中指定商品下的用戶評(píng)論進(jìn)行爬取，對(duì)數(shù)據(jù)預(yù)處理操作后進(jìn)行文本情感分析，感興趣的朋友可以了解下

項(xiàng)目地址

https://github.com/DA1YAYUAN/JD-comments-sentiment-analysis

爬取京東商城中指定商品下的用戶評(píng)論，對(duì)數(shù)據(jù)預(yù)處理后基于SnowNLP的sentiment模塊對(duì)文本進(jìn)行情感分析。

運(yùn)行環(huán)境

Mac OS X
Python3.7 requirements.txt
Pycharm

運(yùn)行方法

數(shù)據(jù)爬?。╦d.comment.py)

啟動(dòng)jd_comment.py，建議修改jd_comment.py中變量user-agent為自己瀏覽器用戶代理
輸入京東商品完整URL
得到京東評(píng)論詞云，存放于jd_ciyun.jpg（詞云輪廓形狀存放于jdicon.jpg)
得到京東評(píng)論數(shù)據(jù)，存放于jd_comment.csv

import os
import time
import json
import random
import csv
import re

import jieba
import requests
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from wordcloud import WordCloud

# 詞云形狀圖片
WC_MASK_IMG = 'jdicon.jpg'
# 評(píng)論數(shù)據(jù)保存文件
COMMENT_FILE_PATH = 'jd_comment.txt'
# 詞云字體
WC_FONT_PATH = '/Library/Fonts/Songti.ttc'


def spider_comment(page=0, key=0):
    """
    爬取京東指定頁(yè)的評(píng)價(jià)數(shù)據(jù)
    :param page: 爬取第幾，默認(rèn)值為0
    """

    url = 'https://club.jd.com/comment/productPageComments.action?callback=fetchJSON_comment98vv4646&productId=' + key + '' \
          '&score=0&sortType=5&page=%s&pageSize=10&isShadowSku=0&fold=1' % page
    kv = {'user-agent': 'Mozilla/5.0', 'Referer': 'https://item.jd.com/'+ key + '.html'}#原本key不輸入值，默認(rèn)為《三體》

    try:
        r = requests.get(url, headers=kv)
        r.raise_for_status()
    except:
        print('爬取失敗')
    # 截取json數(shù)據(jù)字符串
    r_json_str = r.text[26:-2]
    # 字符串轉(zhuǎn)json對(duì)象
    r_json_obj = json.loads(r_json_str)
    # 獲取評(píng)價(jià)列表數(shù)據(jù)
    r_json_comments = r_json_obj['comments']
    # 遍歷評(píng)論對(duì)象列表
    for r_json_comment in r_json_comments:
        # 以追加模式換行寫(xiě)入每條評(píng)價(jià)
        with open(COMMENT_FILE_PATH, 'a+') as file:
            file.write(r_json_comment['content'] + '\n')
        # 打印評(píng)論對(duì)象中的評(píng)論內(nèi)容
        print(r_json_comment['content'])


def batch_spider_comment():
    """
        批量爬取某東評(píng)價(jià)
        """
    # 寫(xiě)入數(shù)據(jù)前先清空之前的數(shù)據(jù)
    if os.path.exists(COMMENT_FILE_PATH):
        os.remove(COMMENT_FILE_PATH)
    key = input("Please enter the address:")
    key = re.sub("\D","",key)
    #通過(guò)range來(lái)設(shè)定爬取的頁(yè)面數(shù)
    for i in range(10):
        spider_comment(i,key)
        # 模擬用戶瀏覽，設(shè)置一個(gè)爬蟲(chóng)間隔，防止ip被封
        time.sleep(random.random() * 5)


def cut_word():
    """
    對(duì)數(shù)據(jù)分詞
    :return: 分詞后的數(shù)據(jù)
    """
    with open(COMMENT_FILE_PATH) as file:
        comment_txt = file.read()
        wordlist = jieba.cut(comment_txt, cut_all=False)#精確模式
        wl = " ".join(wordlist)
        print(wl)
        return wl


def create_word_cloud():
    """44144127306
    生成詞云
    :return:
    """
    # 設(shè)置詞云形狀圖片
    wc_mask = np.array(Image.open(WC_MASK_IMG))
    # 設(shè)置詞云的一些配置，如：字體，背景色，詞云形狀，大小
    wc = WordCloud(background_color="white", max_words=2000, mask=wc_mask, scale=4,
                   max_font_size=50, random_state=42, font_path=WC_FONT_PATH)
    # 生成詞云
    wc.generate(cut_word())
    # 在只設(shè)置mask的情況下,你將會(huì)得到一個(gè)擁有圖片形狀的詞云
    plt.imshow(wc, interpolation="bilinear")
    plt.axis("off")
    plt.figure()
    plt.show()
    wc.to_file("jd_ciyun.jpg")


def txt_change_to_csv():
    with open('jd_comment.csv', 'w+', encoding="utf8", newline='')as c:
        writer_csv = csv.writer(c, dialect="excel")
        with open("jd_comment.txt", 'r', encoding='utf8')as f:
            # print(f.readlines())
            for line in f.readlines():
                # 去掉str左右端的空格并以空格分割成list
                line_list = line.strip('\n').split(',')
                print(line_list)
                writer_csv.writerow(line_list)

if __name__ == '__main__':
    # 爬取數(shù)據(jù)
    batch_spider_comment()

    #轉(zhuǎn)換數(shù)據(jù)
    txt_change_to_csv()

    # 生成詞云
    create_word_cloud()

模型訓(xùn)練（train.py）

準(zhǔn)備正負(fù)語(yǔ)料集online_shopping_10_cats.csv，分別存入negative.txt和positive.txt
啟動(dòng)train.py，新建文件sentiment.marshal，存入訓(xùn)練后的模型
找到外部庫(kù)中snownlp中sentiment模塊，將訓(xùn)練得到的sentiment.marshal.3文件覆蓋sentiment模塊中自帶的sentiment.marshal.3

# -*-coding:utf-8-*-

def train():
    from snownlp import sentiment
    print("開(kāi)始訓(xùn)練數(shù)據(jù)集...")
    sentiment.train('negative.txt', 'positive.txt')#自己準(zhǔn)備數(shù)據(jù)集
    sentiment.save('sentiment.marshal')#保存訓(xùn)練模型
    #python2保存的是sentiment.marshal；python3保存的是sentiment.marshal.3
    "訓(xùn)練完成后，將訓(xùn)練完的模型，替換sentiment中的模型"

def main():
    train()  # 訓(xùn)練正負(fù)向商品評(píng)論數(shù)據(jù)集
    print("數(shù)據(jù)集訓(xùn)練完成！")

if __name__ == '__main__':
    main()

情感分析（sentiment.analysis.py）

啟動(dòng)sentiment.analysis.py
開(kāi)始對(duì)jd_comment.csv中評(píng)論進(jìn)行數(shù)據(jù)處理，處理后文件存入processed_comment_data.csv
sentiment模塊根據(jù)sentiment.marshal.3對(duì)評(píng)論進(jìn)行情感評(píng)分，評(píng)分結(jié)果存入result.csv
評(píng)分結(jié)果可視化，生成文件fig.png

from snownlp import sentiment
import pandas as pd
import snownlp
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

#from word_cloud import word_cloud_creation, word_cloud_implementation, word_cloud_settings

def read_csv():
    '''讀取商品評(píng)論數(shù)據(jù)文件'''
    comment_data = pd.read_csv('jd_comment.csv', encoding='utf-8',
                               sep='\n', index_col=None)
    #返回評(píng)論作為參數(shù)
    return comment_data


def clean_data(data):
    '''數(shù)據(jù)清洗'''
    df = data.dropna()  # 消除缺失數(shù)據(jù) NaN為缺失數(shù)據(jù)
    df = pd.DataFrame(df.iloc[:, 0].unique())  # 數(shù)據(jù)去重
    return df
    # print('數(shù)據(jù)清洗后：', len(df))


def clean_repeat_word(raw_str, reverse=False):
    '''去除評(píng)論中的重復(fù)使用的詞匯'''
    if reverse:
        raw_str = raw_str[::-1]
    res_str = ''
    for i in raw_str:
        if i not in res_str:
            res_str += i
    if reverse:
        res_str = res_str[::-1]
    return res_str


def processed_data(filename):
    '''清洗完畢的數(shù)據(jù)，并保存'''
    df = clean_data(read_csv())#數(shù)據(jù)清洗
    ser1 = df.iloc[:, 0].apply(clean_repeat_word)#去除重復(fù)詞匯
    df2 = pd.DataFrame(ser1.apply(clean_repeat_word, reverse=True))
    df2.to_csv(f'{filename}.csv', encoding='utf-8', index_label=None, index=None)


def train():
    '''訓(xùn)練正向和負(fù)向情感數(shù)據(jù)集，并保存訓(xùn)練模型'''
    sentiment.train('negative.txt', 'positive.txt')
    sentiment.save('seg.marshal')#python2保存的是sentiment.marshal；python3保存的是sentiment.marshal.3


sentiment_list = []

res_list = []


def test(filename, to_filename):
    '''商品評(píng)論-情感分析-測(cè)試'''
    with open(f'{filename}.csv', 'r', encoding='utf-8') as fr:
        for line in fr.readlines():
            s = snownlp.SnowNLP(line)
            #調(diào)用snownlp中情感評(píng)分s.sentiments
            if s.sentiments > 0.6:
                res = '喜歡'
                res_list.append(1)
            elif s.sentiments < 0.4:
                res = '不喜歡'
                res_list.append(-1)
            else:
                res = '一般'
                res_list.append(0)
            sent_dict = {
                '情感分析結(jié)果': s.sentiments,
                '評(píng)價(jià)傾向': res,
                '商品評(píng)論': line.replace('\n', '')
            }
            sentiment_list.append(sent_dict)
            print(sent_dict)
        df = pd.DataFrame(sentiment_list)
        df.to_csv(f'{to_filename}.csv', index=None, encoding='utf-8',
                  index_label=None, mode='w')


def data_virtualization():
    '''分析結(jié)果可視化，以條形圖為測(cè)試樣例'''
    font = FontProperties(fname='/System/Library/Fonts/Supplemental/Songti.ttc', size=14)
    likes = len([i for i in res_list if i == 1])
    common = len([i for i in res_list if i == 0])
    unlikes = len([i for i in res_list if i == -1])

    plt.bar([1], [likes], label='喜歡')#（坐標(biāo)，評(píng)論長(zhǎng)度，名稱）
    plt.bar([2], [common], label='一般')
    plt.bar([3], [unlikes], label='不喜歡')

    x=[1,2,3]
    label=['喜歡','一般','不喜歡']
    plt.xticks(x, label)

    plt.legend()#插入圖例
    plt.xlabel('評(píng)價(jià)種類(lèi)')
    plt.ylabel('評(píng)價(jià)數(shù)目')
    plt.title(u'商品評(píng)論情感分析結(jié)果-條形圖', FontProperties=font)
    plt.savefig('fig.png')
    plt.show()
'''
def word_cloud_show():
    #將商品評(píng)論轉(zhuǎn)為高頻詞匯的詞云
    wl = word_cloud_creation('jd_comment.csv')
    wc = word_cloud_settings()
    word_cloud_implementation(wl, wc)
'''

def main():
     processed_data('processed_comment_data')#數(shù)據(jù)清洗
     #train()  # 訓(xùn)練正負(fù)向商品評(píng)論數(shù)據(jù)集

     test('jd_comment', 'result')

     print('數(shù)據(jù)可視化中...')
     data_virtualization()  # 數(shù)據(jù)可視化

     print('python程序運(yùn)行結(jié)束。')

if __name__ == '__main__':
    main()