给定这样一个数据框架，包括项目和相应的评审文本：
item_id review_text
B2JLCNJF16 i was attracted to this...
B0009VEM4U great snippers...
我想映射review_text中5000最频繁的单词，所以得到的数据帧应该是：
item_id review_text
B2JLCNJF16 1 2 3 4 5...
B0009VEM4U 6... #as the word "snippers" is out of the top 5000 most frequent word
或者，一袋单词矢量是非常可取的：
item_id review_text
B2JLCNJF16 [1,1,1,1,1....]
B0009VEM4U [0,0,0,0,0,1....]
我该怎么做？非常感谢！
编辑：我试过@ayhan的答案。现在我已经成功地将审查文本更改为doc2bow形式：
item_id review_text
B2JLCNJF16 [(123,2),(130,3),(159,1)...]
B0009VEM4U [(3,2),(110,2),(121,5)...]
它表示ID为123的单词在该文档中已出现2次。现在我想把它转移到一个向量上，比如：
[0,0,0,.....,2,0,0,0,....,3,0,0,0,......1...]
#123rd 130th 159th
你怎么做的？提前谢谢！
首先，要获得每行中的单词列表：
df["review_text"] = df["review_text"].map(lambda x: x.split(' '))
现在您可以将df["review_text"]传递到gensim的Dictionary:
from gensim import corpora
dictionary = corpora.Dictionary(df["review_text"])
对于5000个最频繁的单词，使用filter_extremes方法：
dictionary.filter_extremes(no_below=1, no_above=1, keep_n=5000)
doc2bow方法将为您提供单词表示（word_id，frequency）：
df["bow"] = df["review_text"].map(dictionary.doc2bow)
0 [(1, 2), (3, 1), (5, 1), (11, 1), (12, 3), (18...
1 [(0, 3), (24, 1), (28, 1), (30, 1), (56, 1), (...
2 [(8, 1), (15, 1), (18, 2), (29, 1), (36, 2), (...
3 [(69, 1), (94, 1), (115, 1), (123, 1), (128, 1...
4 [(2, 1), (18, 4), (26, 1), (32, 1), (55, 1), (...
5 [(6, 1), (18, 1), (30, 1), (61, 1), (71, 1), (...
6 [(0, 5), (13, 1), (18, 6), (31, 1), (42, 1), (...
7 [(0, 10), (5, 1), (18, 1), (35, 1), (43, 1), (...
8 [(0, 24), (1, 4), (4, 2), (7, 1), (10, 1), (14...
9 [(0, 7), (18, 3), (30, 1), (32, 1), (34, 1), (...
10 [(0, 5), (9, 1), (18, 3), (19, 1), (21, 1), (2...
在获得单词包表示后，您可以在每行中插入序列（可能不是很有效）：
df2 = pd.concat([pd.DataFrame(s).set_index(0) for s in df["bow"]], axis=1).fillna(0).T.set_index(df.index)
0 1 2 3 4 5 6 7 8 9 ... 728 729 730 731 732 733 734 735 736 737
0 0 2 0 1 0 1 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 3 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 1 0 ... 0 0 0 0 0 1 1 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 0 0 1 0 0 0 0 0 0 0 ... 0 0 0 0 0 1 0 0 1 0
5 0 0 0 0 0 0 1 0 0 0 ... 0 0 0 1 0 0 0 0 0 0
6 5 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 0 0 0 0 0 0
7 10 0 0 0 0 1 0 0 0 0 ... 0 0 0 0 0 0 0 1 0 0
8 24 4 0 0 2 0 0 1 0 0 ... 1 1 2 0 1 3 1 0 1 0
9 7 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 1
10 5 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0