The best way to troubleshoot this is to actually look in Wordnet. Take a look here: Loving in wordnet. As you can see, there is actually an adjective "loving" present in Wordnet. As a matter of fact, there is even the adverb "lovingly": lovingly in Wordnet. Because wordnet doesn't actually know what part of speech you actually want, it defaults to noun ('n' in Wordnet). If you are using Penn Treebank tag set, here's some handy function for transforming Penn to WN tags:

from nltk.corpus import wordnet as wn

def is_noun(tag):
    return tag in ['NN', 'NNS', 'NNP', 'NNPS']


def is_verb(tag):
    return tag in ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']


def is_adverb(tag):
    return tag in ['RB', 'RBR', 'RBS']


def is_adjective(tag):
    return tag in ['JJ', 'JJR', 'JJS']


def penn_to_wn(tag):
    if is_adjective(tag):
        return wn.ADJ
    elif is_noun(tag):
        return wn.NOUN
    elif is_adverb(tag):
        return wn.ADV
    elif is_verb(tag):
        return wn.VERB
    return None

Hope this helps.

Steps to convert : Document->Sentences->Tokens->POS->Lemmas

import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet

#example text text = 'What can I say about this place. The staff of these restaurants is nice and the eggplant is not bad'

class Splitter(object):
    """
    split the document into sentences and tokenize each sentence
    """
    def __init__(self):
        self.splitter = nltk.data.load('tokenizers/punkt/english.pickle')
        self.tokenizer = nltk.tokenize.TreebankWordTokenizer()

    def split(self,text):
        """
        out : ['What', 'can', 'I', 'say', 'about', 'this', 'place', '.']
        """
        # split into single sentence
        sentences = self.splitter.tokenize(text)
        # tokenization in each sentences
        tokens = [self.tokenizer.tokenize(sent) for sent in sentences]
        return tokens


class LemmatizationWithPOSTagger(object):
    def __init__(self):
        pass
    def get_wordnet_pos(self,treebank_tag):
        """
        return WORDNET POS compliance to WORDENT lemmatization (a,n,r,v) 
        """
        if treebank_tag.startswith('J'):
            return wordnet.ADJ
        elif treebank_tag.startswith('V'):
            return wordnet.VERB
        elif treebank_tag.startswith('N'):
            return wordnet.NOUN
        elif treebank_tag.startswith('R'):
            return wordnet.ADV
        else:
            # As default pos in lemmatization is Noun
            return wordnet.NOUN

    def pos_tag(self,tokens):
        # find the pos tagginf for each tokens [('What', 'WP'), ('can', 'MD'), ('I', 'PRP') ....
        pos_tokens = [nltk.pos_tag(token) for token in tokens]

        # lemmatization using pos tagg   
        # convert into feature set of [('What', 'What', ['WP']), ('can', 'can', ['MD']), ... ie [original WORD, Lemmatized word, POS tag]
        pos_tokens = [ [(word, lemmatizer.lemmatize(word,self.get_wordnet_pos(pos_tag)), [pos_tag]) for (word,pos_tag) in pos] for pos in pos_tokens]
        return pos_tokens

lemmatizer = WordNetLemmatizer()
splitter = Splitter()
lemmatization_using_pos_tagger = LemmatizationWithPOSTagger()

#step 1 split document into sentence followed by tokenization
tokens = splitter.split(text)

#step 2 lemmatization using pos tagger 
lemma_pos_token = lemmatization_using_pos_tagger.pos_tag(tokens)
print(lemma_pos_token)