WordNetlemmatizer error - all alphabets are lemmatized

Question

I am trying to lemmatize my dataset for sentiment analysis - What should I do to get the expected output rather than the current output? Input file is a csv - stored as DataFrame object.

dataset = pd.read_csv('xyz.csv')

Here is my code

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
list1_ = []
for file_ in dataset:
    result1 = dataset['Content'].apply(lambda x: [lemmatizer.lemmatize(y) for y in x])
    list1_.append(result1)
dataset = pd.concat(list1_, ignore_index=True)

Expected

>> lemmatizer.lemmatize('cats')
>> [cat]

Current output

>> lemmatizer.lemmatize('cats')
>> [c,a,t,s]

Answer 1

TL;DR

result1 = dataset['Content'].apply(lambda x: [lemmatizer.lemmatize(y) for y in x.split()])

Lemmatizer takes in any string as an input.

If dataset['Content'] columns are strings, iterating through a string would be iterating through the characters not "words", e.g.

>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> x = 'this is a foo bar sentence, that is of type str'
>>> [wnl.lemmatize(ch) for ch in x]
['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 'f', 'o', 'o', ' ', 'b', 'a', 'r', ' ', 's', 'e', 'n', 't', 'e', 'n', 'c', 'e', ',', ' ', 't', 'h', 'a', 't', ' ', 'i', 's', ' ', 'o', 'f', ' ', 't', 'y', 'p', 'e', ' ', 's', 't', 'r']

So you would have to first word tokenize your sentence string, e.g.:

>>> from nltk import word_tokenize
>>> [wnl.lemmatize(word) for word in x.split()]
['this', 'is', 'a', 'foo', 'bar', 'sentence,', 'that', 'is', 'of', 'type', 'str']
>>> [wnl.lemmatize(ch) for ch in word_tokenize(x)]
['this', 'is', 'a', 'foo', 'bar', 'sentence', ',', 'that', 'is', 'of', 'type', 'str']

another e.g.

>>> from nltk import word_tokenize
>>> x = 'the geese ran through the parks'
>>> [wnl.lemmatize(word) for word in x.split()]
['the', u'goose', 'ran', 'through', 'the', u'park']
>>> [wnl.lemmatize(ch) for ch in word_tokenize(x)]
['the', u'goose', 'ran', 'through', 'the', u'park']

But to get a more accurate lemmatization, you should get the sentence word tokenized and pos-tagged, see https://github.com/alvations/earthy/blob/master/FAQ.md#how-to-use-default-nltk-functions-in-earthy