word2vec example using tweet data

word2vec tweets example.py

import pandas as pd
import re
import numpy as np
import nltk 
import gensim


#import data. contains identifier and tweet

tweets=pd.DataFrame.from_csv('tweets.txt', sep='\t', index_col=False)

#data prep
#cleaning

#lower case
clean= tweets['tweet'].str.lower()

#untranslated symbols
clean = clean.str.replace('amp', ' ')
clean = clean.str.replace('quot', ' ')

#keep words whitespace and '
clean = clean.str.replace(r'[^\w\s\']','')

#remove numerics
clean=clean.str.replace(r'[\d]','')

sentences = clean.tolist()

tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
model = gensim.models.Word2Vec(tokenized_sentences, min_count=10)


model.most_similar(positive=['moon'], topn=1)
model.most_similar(positive=['moon'], negative=['poor'], topn=5)
model.most_similar(positive=['moon', 'bench'], topn=5)

model.similarity('john', 'lewis')
model.similarity('bench', 'moon')

If you can update the 'tweet.txt' file location, it would be great.