Challenges
Learn about irrelevant text data challenges and how to handle them using Python.
Loss of contextual information
Loss of contextual information is a significant challenge in removing irrelevant text data during preprocessing. When we remove certain words from a sentence without considering the context, we risk losing important information that may be necessary for understanding the meaning of the text. For example, consider the sentence, “I am reading a book about Python.” If we remove the words “a” and “book,” because they are irrelevant, we end up with “I am reading about Python,” which no longer conveys the initial meaning. Here’s an implementation of this example using Python:
sentence = "I am reading a book about Python"stop_words = set(["a", "book"])words = sentence.split()words_filtered = [word for word in words if word.lower() not in stop_words]filtered_sentence = " ".join(words_filtered)print(filtered_sentence)
Let’s review the code line by line:
Line 1: We start by initializing a variable named
sentence
with the valueI am reading a book about Python
.Line 2: We create a set named
stop_words
containing the two irrelevant words.Line 3: We split the string stored in
sentence
into a list of words and assign it to thewords
variable.Line 4: We create a new list called
words_filtered
using a list comprehension. For eachword
inwords
, we check if the lowercase version ofword
is not in thestop_words
set. If it’s not, we includeword
in thewords_filtered
list.Line 5: We join the words in the
words_filtered
list back into a single string, separated by spaces, and assign it to thefiltered_sentence
variable.Line 6: Finally, we print the value stored in
filtered_sentence
. ...