基于Python爬虫的歌手歌词分析可视化(附源码)

''' Function: 歌手歌词分析 ''' import os import jieba import pickle import numpy as np from PIL import Image from musicdl import musicdl from snownlp import SnowNLP from wordcloud import WordCloud '''歌手歌词分析''' class SingerLyricsAnalysis(): def __init__(self): self.rootdir = os.path.split(os.path.abspath(__file__))[0] self.savedir = None self.savename = 'infos.pkl' self.target_srcs = ['migu'] self.config = {'logfilepath': 'musicdl.log', 'savedir': 'downloaded', 'search_size_per_source': 20, 'proxies': {}} '''运行''' def run(self): while True: singer_name = input('请输入歌手名: ') print('[INFO]: 正在爬取歌手%s的相关数据...' % singer_name) infos = self.crawler(singer_name) print('[INFO]: 正在生成歌手%s的分析结果...' % singer_name) self.analysis(infos) '''爬虫代码''' def crawler(self, singer_name): self.savedir, page, infos = singer_name, 0, [] while True: page += 1 self.config['page'] = page api = musicdl.musicdl(config=self.config) results = api.search(singer_name, self.target_srcs) if len(results[self.target_srcs[0]]) < 1: break infos.append(results) self.save(infos) return infos '''数据分析''' def analysis(self, infos): # 数据清洗 lyrics = [] for info in infos: for item in info[self.target_srcs[0]]: lyric = item['lyric'] lyric = lyric.split('\r\n') lyric_filtered = [] for sentence in lyric: sentence = sentence[10:] if (not sentence) or ('：' in sentence) or (self.savedir in sentence) or ('[' in sentence) or (']' in sentence) or ('歌曲' in sentence): continue lyric_filtered.append(sentence) lyrics += lyric_filtered # 词频分析 words_dict = {} for sentence in lyrics: words = jieba.cut(sentence) for word in words: word = word.strip() if not word: continue if len(word) < 2: continue if word in words_dict: words_dict[word] += 1 else: words_dict[word] = 1 words_freq_sorted = sorted(words_dict.items(), key=lambda item: item[1]) words_freq_top10 = words_freq_sorted[-10:] self.generatewordcloud(words_dict) self.drawbar('%s歌曲中的词语TOP10' % self.savedir, words_freq_top10) # 情感分析 nlp_dict = {'内容极度负面': 0, '内容较为负面': 0, '内容中性': 0, '内容较为正面': 0, '内容非常正面': 0} for sentence in lyrics: score = SnowNLP(sentence).sentiments if score < 0.2: nlp_dict['内容极度负面'] += 1 elif score >= 0.2 and score < 0.4: nlp_dict['内容较为负面'] += 1 elif score >= 0.4 and score < 0.6: nlp_dict['内容中性'] += 1 elif score >= 0.6 and score < 0.8: nlp_dict['内容较为正面'] += 1 else: nlp_dict['内容非常正面'] += 1 self.drawpie('%s的歌词情感分析' % self.savedir, nlp_dict) '''画柱状图''' def drawbar(self, title, infos): from pyecharts.charts import Bar from pyecharts import options as opts from pyecharts.globals import ThemeType bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) bar.add_xaxis([item[0] for item in infos]) bar.add_yaxis('freq', [item[1] for item in infos]) bar.set_global_opts(title_opts=opts.TitleOpts(title=title)) bar.render(os.path.join(self.savedir, title+'.html')) '''画饼图''' def drawpie(self, title, infos): from pyecharts.charts import Pie from pyecharts import options as opts pie = Pie(init_opts=dict(theme='westeros', page_title=title)).add(title, data_pair=tuple(zip(infos.keys(), infos.values())), rosetype='area') pie.set_global_opts(title_opts=opts.TitleOpts(title=title)) pie.render(os.path.join(self.savedir, '%s.html' % title)) '''生成词云''' def generatewordcloud(self, infos): mask = Image.open(os.path.join(self.rootdir, 'resources/mask.jpg')) mask = np.array(mask) wc = WordCloud(background_color='white', font_path=os.path.join(self.rootdir, 'resources/font_cn.TTF'), mask=mask) result = wc.generate_from_frequencies(infos) result.to_file(os.path.join(self.savedir, 'wordcloud.png')) '''保存数据''' def save(self, infos): if not os.path.exists(self.savedir): os.mkdir(self.savedir) fp = open(os.path.join(self.savedir, self.savename), 'wb') pickle.dump(infos, fp) fp.close() return True '''导入数据''' def load(self): fp = open(os.path.join(self.savedir, self.savename), 'rb') return pickle.load(fp) '''run''' if __name__ == '__main__': client = SingerLyricsAnalysis() client.run()