# load train data file to get the tweets
with open('../tweetsCrawler/train.csv', 'r') as train_data:
#write tweets and its matrix to vectors.csv
with open('./vectors.csv', 'w') as vectors_data:
writer = csv.writer(vectors_data)
#write headr row to vectors.csv
writer.writerow([
"politician_name", "party", "tweet", "matrix",
"percentageOfMissingWords"
])
#rows to be written to vectors.csv
out_rows = []
reader = csv.DictReader(train_data)
for row in reader:
current_tweet = Tweet(row['tweet'])
#preprocess the tweet, and get list of tokens
current_tweet_tokens = current_tweet.getTokens()
# get the corresponding matrix for the current_tweet
tweet_matrix, percentageOfMissingWords = word_vectorizer.getMatrix(
current_tweet_tokens)
out_rows.append([
row["politician_name"], row["party"], row["tweet"],
tweet_matrix, percentageOfMissingWords
])
#write output rows to vectors.csv
writer.writerows(out_rows)
