Weights for keywords in a set of documents using Term Frequency and Inverse Document Frequency
TF - IDF stands for  term frequency–inverse document frequency
TF counts the frequency of a term / total #terms in a given document. For each term in a document, this value changes.
IDF counts the log of ratio of total document / term appearing in #documents . This value is constant for a given unique term. Greater the idf value for a term, higher its significance.
Example:  


  Document 1: This is a sample example.
  
  Document 2: This is another example.


Lets calculate for term = "is":
TF(is, Document 1) = 1/5
TF(is, Document 2) = 1/4
IDF(is) = log(2/2) = 0
TFIDF = TF*IDF
TFIDF(is, document 1) = (1/5)*0 = 0
TFIDF(is, document 2) = (1/4)*0 = 0
It means that the term "is" is not a significant term in the list of documents(corpus).
Lets consider term = "another"
TF(another, document 1) = 0/5 = 0
TF(another, document 2) = 1/4
IDF(another) = log(2/1) = 0.301
TFIDF(another, document 1) = 0*0.301 = 0
TFIDF(another, document 2) = (1/4)*0.301
You can observe from both examples that TF varies per document while IDF is constant.
You can convert your entire 270 documents into term document matrix.
Demo in python:  

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

d = pd.Series(['this is a sample example','this is another example'])
df = pd.DataFrame(d)
tfidf_vectorizer = TfidfVectorizer(analyzer='word', min_df=0)
# if you want, say only top 2 features(terms)
# tfidf_vectorizer = TfidfVectorizer(analyzer='word', min_df=0, max_features=2, max_df = 3)
# Terms with given below:
# occurred in too many documents (max_df, tfidf score = 3)  
# occurred in too few documents (min_df, tfidf score = 0)
# cut off by feature selection (max_features, tfidf score = 2).
tfidf = tfidf_vectorizer.fit_transform(df[0])
print tfidf_vectorizer.vocabulary_
# output: {u'this': 4, u'sample': 3, u'is': 2, u'example': 1, u'another': 0}
print tfidf_vectorizer.idf_
# output(constant): [ 1.40546511  1.          1.          1.40546511  1.        ]
print tfidf
# output: 
#(0, 1)        0.448320873199    Document 1, term = example
#(0, 3)        0.630099344518    Document 1, term = sample
#(0, 2)        0.448320873199    Document 1, term = is
#(0, 4)        0.448320873199    Document 1, term = this
#(1, 0)        0.630099344518    Document 2, term = another
#(1, 1)        0.448320873199    Document 2, term = example
#(1, 2)        0.448320873199    Document 2, term = is
#(1, 4)        0.448320873199    Document 2, term = this


Source:
https://en.wikipedia.org/wiki/Tf%E2%80%93idf
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html