如何解决Textblob 过度概括文本并将其归类为中性
我正在使用 textblob 来确定 Twitter 文本的情绪，但有些结果没有极性和主观性（使它们成为中性情绪 - tweets cleaned and placed in pd.dataframe)
This is a chart of the overall contrasts between sentiment (showing way more neutral)
我的代码在下面
# Create a function to get the subjectivity
def getSubjectivity(text):
return TextBlob(text).sentiment.subjectivity
# Create a function to get the polarity
def getPolarity(text):
return TextBlob(text).sentiment.polarity
# Create two new columns 'Subjectivity' & 'Polarity'
df['Subjectivity'] = df['Tweets'].apply(getSubjectivity)
df['Polarity'] = df['Tweets'].apply(getPolarity)
# Show the new dataframe with columns 'Subjectivity' & 'Polarity'
df
# Subjectivity < 1 but > 0 is more factual,> 1 is very opinionated (0 and +1 are min/max)
# Polarity < 0 is more negative,> 0 is more positive (-1 and +1 are the min/max)
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