Text Preprocessing with Python/

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Challenges

Challenges

Learn about irrelevant text data challenges and how to handle them using Python.

We'll cover the following...

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:

Python 3.8
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 value I 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 the words variable.

  • Line 4: We create a new list called words_filtered using a list comprehension. For each word in words, we check if the lowercase version of word is not in the stop_words set. If it’s not, we include word in the words_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 the filtered_sentence variable.

  • Line 6: Finally, we print the value stored in filtered_sentence. ...