Text Preprocessing: A Comprehensive Guide to Noise Removal, Lexicon Normalization, and Object Standardization
Text preprocessing is an essential step for any data analysis process. It is the process of standardizing and cleaning text so that it is ready for further analysis. Text preprocessing involves three main steps: noise removal, lexicon normalization, and object standardization. This article will discuss each step in detail and provide examples of how to perform each step in Python.
What is Text Preprocessing?
Text preprocessing is a process of transforming raw text data into a cleaner, more useful format. It is important to perform text preprocessing before any data analysis or data mining process. This is because unstructured data, such as text, contains a lot of noise. Noise can be in the form of language stopwords, links, URLs, various social media entities, industry-specific words, and punctuations. These types of noise can make it difficult to accurately analyze text data.
Text preprocessing involves three main steps: noise removal, lexicon normalization, and object standardization. Noise removal is the process of removing any irrelevant words or entities from the text. Lexicon normalization is the process of converting multiple representations of the same word into one normalized form. Finally, object standardization is the process of replacing phrases or words that are not in standard lexical dictionaries with their standardized counterparts.
Noise Removal
Noise removal is the first step in text preprocessing. It is the process of removing any irrelevant words or entities from the text. The most common approach for noise removal is to prepare a dictionary of noisy entities and then iterate through the text object by words or tokens. You can then easily eliminate those tokens that are present in your noise dictionary.
In Python, you can run the following code to remove noise from a text. The noise_list should contain the words or entities that you want to remove from the text.
Noise_list = [“an”, “a”, “the”, “…”]
def _remove_noise(input_text):
words = input_text.split()
noise_free_words = [word for word in words if word not in noise_list]
noise_free_text = ” “.join(noise_free_words)
return noise_free_text
_remove_noise(“your sample text”)
“your sample text”
Another approach to noise removal is to use regular expressions. You can use regular expressions to identify patterns in the text and then remove them. For example, the following code will remove any hashtags from a text.
import re
def _remove_regex(input_text, regex_pattern):
urls = re.finditer(regex_pattern, input_text)
for i in urls:
input_text = re.sub(i.group().strip(), ”, input_text)
return input_text
regex_pattern = “#[w]*”
_remove_regex(“remove this #hashtag from sample text”, regex_pattern)
“remove this from sample text”
Lexicon Normalization
Lexicon normalization is the process of converting multiple representations of the same word into one normalized form. This is important because it helps reduce the dimensionality of the text data, making it easier to process.
The most commonly used lexicon normalization techniques are lemmatization and stemming. Lemmatization is a step-by-step process of obtaining the stem or root of the words in addition to words morphological analysis. Stemming is a rule-based process of stripping suffixes from words like es, ing, s, etc. To perform stemming and lemmatization with NLTK, you can run the following code.
from nltk.stem.wordnet import WordNetLemmatizer
lem = WordNetLemmatizer()
from nltk.stem.porter import PorterStemmer
stem = PorterStemmer()
word = “swimming”
lem.lemmatize(word, “v”)
“swim”
stem.stem(word)
“swim”
Object Standardization
Object standardization is the process of replacing phrases or words that are not in standard lexical dictionaries with their standardized counterparts. This is important because it ensures that the text is recognizable by search models and engines.
One way to standardize objects is to use manually prepared data dictionaries. You can also use dictionary lookup methods to replace social media slangs with standardized text. For example, you can use the following code to replace slangs with their standardized counterparts.
Lookup_dict = {‘rt’: ‘Retweet’, ‘dm’:’direct message’, “luv”: “awsm”, “love”: “awesome”, “…”}
def _lookup_words(input_text):
words = input_text.split()
new_words = []
for word in words:
if word.lower() in lookup_dict:
word = lookup_dict[word.lower()]
new_words.append(word)
new_text = ” “.join(new_words)
return new_text
_lookup_words(“RT this is a retweeted tweet”)
“Retweet this is a retweeted tweet”
Other Text Preprocessing Techniques
In addition to the three main text preprocessing techniques discussed above, there are other techniques used for text preprocessing such as grammar checker, encoding-decoding noise, and spelling correction. However, these three text preprocessing methods are the most commonly used when dealing with unstructured data.
Conclusion
Text preprocessing is an essential step in any data analysis process. It involves three main steps: noise removal, lexicon normalization, and object standardization. Noise removal is the process of removing any irrelevant words or entities from the text. Lexicon normalization is the process of converting multiple representations of the same word into one normalized form. Object standardization is the process of replacing phrases or words that are not in standard lexical dictionaries with their standardized counterparts.
In addition to these three main text preprocessing techniques, there are other techniques used for text preprocessing such as grammar checker, encoding-decoding noise, and spelling correction. However, these three text preprocessing methods are the most commonly used when dealing with unstructured data.
By understanding and using text preprocessing techniques, you will be able to easily clean any piece of text data so that it is ready for further analysis.