我有一個資料叫做：
- after_tokenize.xlsx
- 正.xlsx
- negative.xlsx 記號化后 的正 負值
我想要的是為來自 after_tokenize.xlsx 的資料標記正面和負面情緒。如果標記化后的資料有很多來自資料 positive.xlsx 的正面詞，它將是正面的，如果資料有很多來自負面的負面詞，它將是負面的。結果將輸入到名為 label 的標簽中。樣本：
| 資料 | 標簽 |
|---|---|
| [我，喜歡，愛，恨，你] | 積極的 |
| [我，最壞的，討厭，喜歡，你] | 消極的 |
import pandas as pd
import nltk
df = pd.DataFrame({'data': ['i like love hate you', 'i dont hate like you']})
pos = pd.DataFrame(data=['like', 'love'], columns=['positive'])
neg = pd.DataFrame(data=['dont', 'hate'], columns=['negative'])
df['data'] = df.apply(lambda row: nltk.word_tokenize(row['data']), axis=1)
uj5u.com熱心網友回復：
您可以使用set()和 操作set(...) & set(...)來獲取兩個串列中的單詞。
然后你可以使用 len()
len( set([i, like, love, hate, you]) & set(['like', 'love']) )
import pandas as pd
import nltk
df = pd.DataFrame({'data': ['i like love hate you', 'i dont hate like you']})
pos = ['like', 'love']
neg = ['dont', 'hate']
#print(df)
df['data'] = df['data'].apply(nltk.word_tokenize)
# --- get common words ---
df['pos words'] = df['data'].apply(lambda item: list(set(item) & set(pos)))
df['neg words'] = df['data'].apply(lambda item: list(set(item) & set(neg)))
# --- count common words ---
df['pos'] = df['data'].apply(lambda item: len(set(item) & set(pos)))
df['neg'] = df['data'].apply(lambda item: len(set(item) & set(neg)))
# or
df['pos'] = df['pos words'].apply(len)
df['neg'] = df['neg words'].apply(len)
# --- assing labels ---
df['label'] = '???' # default value
#df.['label'][ df['pos'] > df['neg'] ] = 'positive'
df.loc[ (df['pos'] > df['neg']), 'label' ] = 'positive'
#df.['label'][ df['pos'] < df['neg'] ] = 'negative'
df.loc[ (df['pos'] < df['neg']), 'label' ] = 'negative'
# ---
print(df)
結果：
data pos words neg words pos neg label
0 [i, like, love, hate, you] [love, like] [hate] 2 1 positive
1 [i, dont, hate, like, you] [like] [hate, dont] 1 2 negative
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