python - 如何浏览数据框并对文本进行正面或负面分类？

Question

我目前有一个 pandas 数据框，其中包含标记化的推文。

我需要能够浏览每条推文并确定它是正面还是负面，以便我添加包含正面或负面单词的后续列。

示例数据:

tokenized_tweets =  ['football, was, good, we, played, well' , 'We, were, unlucky, today, bad, luck' , 'terrible, performance, bad, game'] 

我需要能够通过 tokenized_tweets 部分运行一个循环，以确定它是积极的还是消极的。

对于示例的情况，正面和负面词如下:

Positive_words = ['good', 'great'] 
Negative_words = ['terrible, 'bad']

所需的输出是一个数据帧，其中包含推文、每条推文包含多少个正面字母、每条推文包含多少个负面字母以及该推文是正面、负面还是中性。

需要根据推文是否包含更多积极或消极的流行语来确定积极消极和中性

期望的输出:

Tokenized tweet                    positive words       negative words         overall 
`football, was, good, we, played, well         1                0            positive` 

We, were, unlucky, today, bad, luck            0                1            negative
terrible, performance, bad, game               0                2            negative

Answer 1

import pandas as pd
import numpy as np

df = pd.DataFrame({'tokenized_tweets': ['football, was, good, we, played, well', 'We, were, unlucky, today, bad, luck','terrible, performance, bad, game']})

Positive_words = ['good', 'great'] 
Negative_words = ['terrible','bad']

df['positive words'] = df['tokenized_tweets'].str.count('|'.join(Positive_words))
df['negative words'] = df['tokenized_tweets'].str.count('|'.join(Negative_words))

conditions = [
(df['positive words'] > df['negative words']),
(df['negative words'] > df['positive words']),
(df['negative words'] == df['positive words'])
]

choices = [
'positive',
'negative',
'neutral'
]

df['overall'] = np.select(conditions, choices, default = '')

df

输出:

tokenized_tweets                      positive words   negative words   overall
0   football, was, good, we, played, well   1               0        positive
1   We, were, unlucky, today, bad, luck     0               1        negative
2   terrible, performance, bad, game        0               2        negative