Two questions about word2vec and gensim

I’ve written the code below to try word2vec implementation of gensim. I’ve two questions:

1. Even though I’ve removed stop words, the word "the" is listed as one of the most similar words of "friend".
2. The most similar words of "friend" is not satisfying (at least according to my subjective evaluation). Should I try a larger text (austen-emma.txt file contains 192427 words) or the problem is something else?

Thanks.

import nltk
from nltk.tokenize import sent_tokenize
from nltk.corpus import gutenberg
import gensim 
from gensim.models import Word2Vec 
from gensim.parsing.preprocessing import remove_stopwords
from nltk.tokenize import RegexpTokenizer

text = gutenberg.raw('austen-emma.txt'). 
text = remove_stopwords(text). 
tokenizer = RegexpTokenizer(r'w+'). 
data = []. 

for i in sent_tokenize(text):     
    temp = []. 
    for j in tokenizer.tokenize(i):   
       temp.append(j.lower()).  
    data.append(temp).  

model = gensim.models.Word2Vec(data, min_count = 1,  
                          size = 32, window = 2) 

model.wv.most_similar(positive='friend', topn=10)


[('mind', 0.9998476505279541),  
 ('present', 0.9998302459716797),  
 ('till', 0.9998292326927185),  
 ('herself', 0.9998183250427246),  
 ('highbury', 0.999806821346283),  
 ('the', 0.9998062252998352),  
 ('place', 0.9998047351837158),  
 ('house', 0.999799907207489),  
 ('her', 0.9997915029525757),  
 ('me', 0.9997879266738892)]