目錄項(xiàng)目地址運(yùn)行環(huán)境運(yùn)行方法數(shù)據(jù)爬?。╦d.comment.py)模型訓(xùn)練（train.py）情感分析（sentiment.analysis.py）詞云輪廓圖商品評論詞云情感分析結(jié)果可視化項(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論，對數(shù)據(jù)預(yù)處理后基于SnowNLP的sentiment模塊對文本進(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) 得到京東評論數(shù)據(jù)，存放于jd_comment.csv

import osimport timeimport jsonimport randomimport csvimport reimport jiebaimport requestsimport numpy as npfrom PIL import Imageimport matplotlib.pyplot as pltfrom wordcloud import WordCloud# 詞云形狀圖片WC_MASK_IMG = ’jdicon.jpg’# 評論數(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): ''' 爬取京東指定頁的評價數(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對象 r_json_obj = json.loads(r_json_str) # 獲取評價列表數(shù)據(jù) r_json_comments = r_json_obj[’comments’] # 遍歷評論對象列表 for r_json_comment in r_json_comments:# 以追加模式換行寫入每條評價with open(COMMENT_FILE_PATH, ’a+’) as file: file.write(r_json_comment[’content’] + ’n’)# 打印評論對象中的評論內(nèi)容print(r_json_comment[’content’])def batch_spider_comment(): '''批量爬取某東評價''' # 寫入數(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range來設(shè)定爬取的頁面數(shù) for i in range(10):spider_comment(i,key)# 模擬用戶瀏覽，設(shè)置一個爬蟲間隔，防止ip被封time.sleep(random.random() * 5)def cut_word(): ''' 對數(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 wldef 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,你將會得到一個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listline_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)語料集online_shopping_10_cats.csv，分別存入negative.txt和positive.txt 啟動train.py，新建文件sentiment.marshal，存入訓(xùn)練后的模型 找到外部庫中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sentiment.analysis.py 開始對jd_comment.csv中評論進(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對評論進(jìn)行情感評分，評分結(jié)果存入result.csv 評分結(jié)果可視化，生成文件fig.png

from snownlp import sentimentimport pandas as pdimport snownlpimport matplotlib.pyplot as pltfrom matplotlib.font_manager import FontProperties#from word_cloud import word_cloud_creation, word_cloud_implementation, word_cloud_settingsdef read_csv(): ’’’讀取商品評論數(shù)據(jù)文件’’’ comment_data = pd.read_csv(’jd_comment.csv’, encoding=’utf-8’, sep=’n’, index_col=None) #返回評論作為參數(shù) return comment_datadef 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_strdef 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.3sentiment_list = []res_list = []def test(filename, to_filename): ’’’商品評論-情感分析-測試’’’ 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,’評價傾向’: res,’商品評論’: 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)，評論長度，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(’評價種類’) plt.ylabel(’評價數(shù)目’) plt.title(u’商品評論情感分析結(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()詞云輪廓圖

商品評論詞云

情感分析結(jié)果可視化

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