def sentiment_analyze(text):
if isinstance(text, float) and math.isnan(text):
text = ''
text = HanziConv.toSimplified(text)
senti = Sentiment()
sentiment_result = senti.sentiment_count(text)
emotion = Emotion()
emotion_result = emotion.emotion_count(text)
emotion_result['樂'] = emotion_result.pop('乐')
emotion_result['懼'] = emotion_result.pop('惧')
emotion_result['惡'] = emotion_result.pop('恶')
emotion_result['驚'] = emotion_result.pop('惊')
return sentiment_result, emotion_result
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import jieba
from wordcloud import WordCloud
from cnsenti import Emotion
from cnsenti import Sentiment
# data= data[0:5]
senti = Sentiment()
emotion = Emotion()
#显示所有列
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
#设置value的显示长度为100，默认为50
pd.set_option('max_colwidth', 100)
# stopword_list_cn = [k.strip() for k in open('../../data/cn_stopwords.txt', encoding='utf8').readlines() if k.strip() != '']
# stopword_list_hit = [k.strip() for k in open('../..//data/hit_stopwords.txt', encoding='utf8').readlines() if k.strip() != '']
# stopword_list_scu = [k.strip() for k in open('../../data/scu_stopwords.txt', encoding='utf8').readlines() if k.strip() != '']
# stopword_list = stopword_list_cn+stopword_list_scu+stopword_list_hit
def emotion_sentiment_convert(x):
result = emotion.emotion_count(x)
result_senti = senti.sentiment_count(x)
return result['好'], result['乐'], result['哀'], result['怒'], result[
'惧'], result['恶'], result['惊'], result_senti['pos'], result_senti[
'neg']
from cnsenti import Sentiment
senti = Sentiment(pos='正面词自定义.txt', #正面词典txt文件相对路径
neg='负面词自定义.txt', #负面词典txt文件相对路径
encoding='utf-8') #两txt均为utf-8编码
test_text = '我好开心啊，非常非常非常高兴！今天我得了一百分，我很兴奋开心，愉快，开心'
result1 = senti.sentiment_count(test_text)
result2 = senti.sentiment_calculate(test_text)
print('sentiment_count',result1)
print('sentiment_calculate',result2)
import jieba
# from snownlp import SnowNLP
# from snownlp import sentiment
from cnsenti import Emotion
from cnsenti import Sentiment
senti = Sentiment()
emotion = Emotion()
test_text = '来得时-甘精胰岛素说明书'
print(jieba.lcut(test_text))
result = emotion.emotion_count(test_text)
result_o = senti.sentiment_count(test_text)
print(result)
print(result_o)
print(senti.sentiment_calculate(test_text))
# print(result['好'])
import pathlib
import pickle
def read_dict(file):
pathchain = ['dictionary', 'dutir', file]
mood_dict_filepath = pathlib.Path(__file__).parent.joinpath(*pathchain)
dict_f = open(mood_dict_filepath, 'rb')
words = pickle.load(dict_f)
return words
print(
read_dict(
'C:/Users\Thinkpad\Anaconda3\Lib\site-packages\cnsenti\dictionary\dutir\惧.pkl'
from cnsenti import Sentiment
senti = Sentiment(
pos='正面词自定义.txt', #正面词典txt文件相对路径
neg='负面词自定义.txt', #负面词典txt文件相对路径
merge=True, #merge=True融合自定义词典和cnsenti自带词典；merge=False只使用自定义词典
encoding='utf-8') #两txt均为utf-8编码
test_text = '这家公司是行业的引领者，是中流砥柱。'
result1 = senti.sentiment_count(test_text)
result2 = senti.sentiment_calculate(test_text)
print('sentiment_count', result1)
print('sentiment_calculate', result2)