lzh
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import jieba
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import os
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def stopwordslist(filepath):
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"""读取停用词表"""
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stopwords = [line.strip() for line in open(filepath, 'r', encoding='gbk').readlines()]
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return stopwords
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def sentiment_dict(filepath):
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"""读取情感词典"""
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res = {}
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with open(filepath, encoding="utf-8") as f:
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lines = f.readlines()
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for line in lines:
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if line.strip():
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k, v = line.split()
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res[k] = float(v)
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return res
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# 对句子去除停用词
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def movestopwords(sentence):
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stopwords = stopwordslist(os.path.join(os.path.abspath("."),'stopword.txt')) # 这里加载停用词的路径
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outstr = ''
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for word in sentence:
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if word not in stopwords:
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if word != '\t' and '\n':
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outstr += word
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content_seg = jieba.cut(outstr)
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return content_seg
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def get_top_positive_negative_frequency(data, top=10):
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"""
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获取top n个消极词汇与积极词汇以及词频
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:param data: 评论数据
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:param top:
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:return:
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"""
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senti_dict = sentiment_dict(os.path.join(os.path.abspath("."),'text_analysis\\BosonNLP_sentiment_score.txt'))
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print(os.path.join(os.path.abspath("."),'BosonNLP_sentiment_score.txt'))
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content_seg = movestopwords(data["comments"])
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scores = {}
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frequency = {}
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for word in content_seg:
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scores[word] = senti_dict.get(word, 0)
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if frequency.get(word):
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frequency[word] += 1
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else:
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frequency[word] = 1
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scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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frequency = dict(sorted(frequency.items(), key=lambda x: x[1], reverse=True))
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positive = dict(scores[0:top])
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negative = dict(scores[-top:])
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return {
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"positive": positive,
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"negative": negative,
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"frequency": frequency
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}
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if __name__ == '__main__':
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# 读取nlp情感词分数
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from utils import get_data
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data = get_data("lzh","1512002")
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print(get_top_positive_negative_frequency(data,15))
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