118 lines
4.8 KiB
Python
118 lines
4.8 KiB
Python
'''
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Function:
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Implementation of SingerLyricsAnalysis
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Author:
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Zhenchao Jin
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WeChat Official Account (微信公众号):
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Charles的皮卡丘
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'''
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import os
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import jieba
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import pickle
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import numpy as np
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from PIL import Image
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from snownlp import SnowNLP
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from wordcloud import WordCloud
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from musicdl.modules.sources import MiguMusicClient
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'''SingerLyricsAnalysis'''
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class SingerLyricsAnalysis():
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def __init__(self):
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self.root_dir = os.path.split(os.path.abspath(__file__))[0]
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self.music_client = MiguMusicClient(search_size_per_source=2000)
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'''start'''
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def start(self):
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while True:
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singer_name = input('Input singer to analyze: ')
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print(f'[INFO]: Searching {singer_name}')
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infos = self.crawler(singer_name)
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print(f'[INFO]: Analyzing {singer_name}')
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self.analysis(infos)
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'''crawler'''
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def crawler(self, singer_name):
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song_infos = self.music_client.search(keyword=singer_name)
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self.save(singer_name=singer_name, song_infos=song_infos)
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return song_infos
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'''analysis'''
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def analysis(self, song_infos):
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# data clean
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lyrics = []
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for song_info in song_infos:
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lyric = song_info['lyric']
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lyric = lyric.split('\r\n')
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lyric_filtered = []
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for sentence in lyric:
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sentence = sentence[10:]
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if (not sentence) or ('：' in sentence) or (self.root_dir in sentence) or ('[' in sentence) or (']' in sentence) or ('歌曲' in sentence): continue
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lyric_filtered.append(sentence)
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lyrics += lyric_filtered
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# generatewordcloud
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words_dict = {}
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for sentence in lyrics:
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words = jieba.cut(sentence)
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for word in words:
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word = word.strip()
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if not word: continue
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if len(word) < 2: continue
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if word in words_dict: words_dict[word] += 1
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else: words_dict[word] = 1
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words_freq_sorted = sorted(words_dict.items(), key=lambda item: item[1])
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words_freq_top10 = words_freq_sorted[-10:]
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self.generatewordcloud(words_dict)
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self.drawbar('%s歌曲中的词语TOP10' % self.root_dir, words_freq_top10)
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# nlp analysis
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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}
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for sentence in lyrics:
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score = SnowNLP(sentence).sentiments
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if score < 0.2:
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nlp_dict['内容极度负面'] += 1
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elif score >= 0.2 and score < 0.4:
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nlp_dict['内容较为负面'] += 1
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elif score >= 0.4 and score < 0.6:
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nlp_dict['内容中性'] += 1
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elif score >= 0.6 and score < 0.8:
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nlp_dict['内容较为正面'] += 1
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else:
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nlp_dict['内容非常正面'] += 1
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self.drawpie('%s的歌词情感分析' % self.root_dir, nlp_dict)
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'''drawbar'''
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def drawbar(self, title, infos):
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from pyecharts.charts import Bar
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from pyecharts import options as opts
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from pyecharts.globals import ThemeType
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bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
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bar.add_xaxis([item[0] for item in infos])
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bar.add_yaxis('freq', [item[1] for item in infos])
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bar.set_global_opts(title_opts=opts.TitleOpts(title=title))
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bar.render(os.path.join(self.root_dir, title+'.html'))
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'''drawpie'''
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def drawpie(self, title, infos):
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from pyecharts.charts import Pie
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from pyecharts import options as opts
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pie = Pie(init_opts=dict(theme='westeros', page_title=title)).add(title, data_pair=tuple(zip(infos.keys(), infos.values())), rosetype='area')
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pie.set_global_opts(title_opts=opts.TitleOpts(title=title))
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pie.render(os.path.join(self.root_dir, '%s.html' % title))
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'''generatewordcloud'''
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def generatewordcloud(self, infos):
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mask = Image.open(os.path.join(self.root_dir, 'resources/mask.jpg'))
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mask = np.array(mask)
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wc = WordCloud(background_color='white', font_path=os.path.join(self.root_dir, 'resources/font_cn.TTF'), mask=mask)
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result = wc.generate_from_frequencies(infos)
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result.to_file(os.path.join(self.root_dir, 'wordcloud.png'))
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'''save'''
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def save(self, song_infos, singer_name):
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data_save_path = os.path.join(self.root_dir, f'song_infos_{singer_name}.pkl')
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with open(data_save_path, 'wb') as fp:
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pickle.dump(song_infos, fp)
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'''load'''
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def load(self, singer_name):
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data_save_path = os.path.join(self.root_dir, f'song_infos_{singer_name}.pkl')
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fp = open(data_save_path, 'rb')
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return pickle.load(fp)
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'''tests'''
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if __name__ == '__main__':
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client = SingerLyricsAnalysis()
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client.start() |