如何使用TextBlob和Python进行标题的情感分析 [英] How to do sentiment analysis of headlines with TextBlob and Python

问题描述

我想计算我拥有的一些头条新闻的极性和主观性. 我的代码工作正常，它没有给出任何错误，但是对于某些行，其极性和主观性给出的结果为0.00000.你知道为什么吗?

I want to calculate the polarity and subjectivity for some headlines that I have. My code works fine, it does not gives any error but for some rows it gives result 0.00000 for polarity and subjectivity. Do you know why?

您可以在此处下载数据表格:

You can download the data form here:

https://www.sendspace.com/file/e8w4tw

我做错什么了吗? 这是代码:

Am I doing something wrong? This is the code:

import pandas as pd
from textblob import TextBlob

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

df = pd.read_excel('coca cola news.xlsx', encoding='utf8')

df = df.dropna().reset_index(drop = True)
df = df.drop_duplicates().reset_index(drop = True)
print(df)

head_sentiment = []
head_subj = []

par_sentiment = []
par_subj = []


df['Headline Sentiment'] =  df['Headline'].apply(lambda text: TextBlob(text).sentiment.polarity).round(4)
df['Headline Subjectivity'] =  df['Headline'].apply(lambda text: TextBlob(text).sentiment.subjectivity).round(4)

df['Paragraph Sentiment'] =  df['Paragraph'].apply(lambda text: TextBlob(text).sentiment.polarity).round(4)
df['Paragraph Subjectivity'] =  df['Paragraph'].apply(lambda text: TextBlob(text).sentiment.subjectivity).round(4)

print(df)

print(df[df.columns[-4:]])

我的意思是，我知道可能是0，但是我在40％-50％的行中得到0.0000，这很多，甚至没有0.00001，这对我来说很奇怪.

I mean, I know that 0 is possible result, but Im getting 0.0000 in 40%-50% of rows, thats a lot, not even 0.00001, that seams strange to me.

你能帮我吗?

推荐答案

有时会发生. 尝试从多语种使用极性方法. https://polyglot.readthedocs.io/en/latest/Installation.html

its sometimes happen. Try to use polarity method from polyglot. https://polyglot.readthedocs.io/en/latest/Installation.html

并比较结果.首先，您应该进行一些预处理，例如:

and compare results. Firstly you should make some preprocessing like:

• 删除停用词
• 删除数字，html链接，数字，特殊字符等

这篇关于如何使用TextBlob和Python进行标题的情感分析的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！