How To Implement Natural Language Processing (NLP) Feature Engineering In Python [8 Techniques]

by | Mar 25, 2023 | Machine Learning, Natural Language Processing

Natural Language Processing (NLP) feature engineering involves transforming raw textual data into numerical features that can be input into machine learning models. Feature engineering is a crucial step in NLP, as it determines the effectiveness of the models built for the task.

In this article, we summarise the 8 most common NLP feature engineering techniques and provide each one’s advantages and disadvantages with code examples in Python to get you started.

We further provide NLP feature engineering techniques specifically for social media data. Social media data is often messy and in a shorter form and requires a different approach.

To make our feature engineering more efficient, we frequently combine multiple pre-processing techniques to create a pre-processing pipeline. Check out our previous blog post on how to create a functional pre-processing pipeline.

NLP feature engineering is like a pipeline with bits and bolts

Feature engineering is like building a pipeline. Lots of bits need to work together to get the final overall result.

Top 8 most common NLP feature engineering techniques

1. Tokenization for NLP feature engineering

This involves splitting text into individual words or tokens. Tokenization is often the first step in NLP feature engineering.

Advantages

  • Tokenization can help simplify the text by reducing it to its most basic components.
  • Tokenization can improve the accuracy of text analysis by providing a consistent basis for comparison.
  • Tokenization can help identify important keywords and phrases in the text that can be used for analysis.
  • Tokenization can help reduce the complexity of text by removing irrelevant information.

Disadvantages

  • Tokenization can sometimes produce meaningless or ambiguous tokens.
  • Tokenization might not always get the meaning of a text right, especially when there are complicated sentence structures or idiomatic expressions.
  • Tokenization may not work well for languages with complex word structures or for languages that do not use spaces to separate words.

Example

Here’s an example in Python using the NLTK library:

import nltk 
from nltk.tokenize import word_tokenize 

text = "The quick brown fox jumped over the lazy dog." 
tokens = word_tokenize(text) print(tokens)

Output:

['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', '.']

In this example, the text is tokenized into individual words or tokens, which can be used for further analysis.

2. Stop word removal for NLP feature engineering

Stop words are common words without meaning, such as “the” or “and”. Removing stop words can reduce the feature space’s dimensionality and improve the model’s efficiency.

Advantages

  • Stop word removal can help reduce the noise in the data and improve the accuracy of text analysis by removing words that are not relevant to the analysis.
  • Stop word removal can help reduce the size of the dataset and improve computational efficiency.
  • Stop word removal can help improve the readability of the text by removing redundant or unnecessary words.

Disadvantages

  • Stop word removal may remove important context and nuance from the text, especially in cases where the context of the text is important.
  • Stop word removal may not work well for all languages, as some rely heavily on function words to convey meaning.
  • Stop word removal may remove words that are not technically stop words but are important for the analysis.

Example

Here’s an example of stop-word removal in Python using the NLTK library:

import nltk 
from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize 

text = "The quick brown fox jumped over the lazy dog." 
stop_words = set(stopwords.words('english')) 
tokens = word_tokenize(text) 
filtered_tokens = [token for token in tokens if not token in stop_words] 
print(filtered_tokens)

Output:

['The', 'quick', 'brown', 'fox', 'jumped', 'lazy', 'dog', '.']

In this example, the stop words in the English language are removed from the text, leaving only the important words for analysis. The resulting tokens can be used for further analysis.

3. Stemming and lemmatization for NLP feature engineering

Stemming involves reducing words to their base form by removing suffixes, while lemmatization involves reducing words to their base form by mapping them to their dictionary form. Both techniques can help reduce the dimensionality of the feature space and improve model accuracy.

Advantages

  • Stemming and lemmatization can help improve the accuracy of text analysis by reducing words to their most basic form.
  • Stemming and lemmatization can help improve the efficiency of text analysis by reducing the number of unique words in the dataset.
  • Stemming and lemmatization can help identify patterns and relationships in the text that may not be apparent when words are not reduced to their base form.

Disadvantages

  • Stemming and lemmatization may result in losing important information, as words reduced to their base form may not accurately capture the text’s intended meaning.
  • Stemming and lemmatization may produce non-words or words that do not exist in the language, making the text more difficult to understand.
  • Stemming and lemmatization may not work well for all languages or text types.

Example

Here’s an example of stemming and lemmatization in Python using the NLTK library:

Stemming

import nltk 
from nltk.stem import PorterStemmer 
from nltk.tokenize import word_tokenize 

text = "The quick brown foxes jumped over the lazy dogs." 
stemmer = PorterStemmer() 
tokens = word_tokenize(text) 
stemmed_tokens = [stemmer.stem(token) for token in tokens] 
print(stemmed_tokens)

Output:

['the', 'quick', 'brown', 'fox', 'jump', 'over', 'the', 'lazi', 'dog', '.']

In this example, the words are reduced to their stem form, which can help identify patterns and relationships in the text.

Lemmatization

import nltk 
from nltk.stem import WordNetLemmatizer 
from nltk.tokenize import word_tokenize 

text = "The quick brown foxes jumped over the lazy dogs." 
lemmatizer = WordNetLemmatizer() 
tokens = word_tokenize(text) 
lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens] 
print(lemmatized_tokens)

Output:

['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', '.']

In this example, the words are reduced to their base form, which can help identify patterns and relationships in the text. Compared to stemming, lemmatization is generally considered to produce more accurate results.

4. N-grams for NLP feature engineering

N-grams are sequences of adjacent words of length n . N-grams can capture more context and help the model better understand the meaning of the text.

Advantages

  • N-grams can help capture the context and meaning of the text by considering the relationship between adjacent words or phrases.
  • N-grams can identify patterns and relationships in the text that may not be apparent when considering individual words or phrases.
  • N-grams can be used to generate predictive models that can help classify or predict text based on patterns or relationships in the data.

Disadvantages

  • N-grams can produce a large number of features, which can make the analysis more computationally intensive and require more memory.
  • N-grams may not work well for all types of text or all languages.
  • N-grams may not capture the full range of meanings of the text, as they are limited to adjacent words or phrases.

Example

Here’s an example of generating n-grams in Python using the NLTK library:

import nltk 
from nltk.util import ngrams 

text = "The quick brown foxes jumped over the lazy dogs." 
tokens = nltk.word_tokenize(text) 
bigrams = ngrams(tokens, 2) trigrams = ngrams(tokens, 3) 
print(list(bigrams)) 
print(list(trigrams))

Output:

[('The', 'quick'), ('quick', 'brown'), ('brown', 'foxes'), ('foxes', 'jumped'), ('jumped', 'over'), ('over', 'the'), ('the', 'lazy'), ('lazy', 'dogs'), ('dogs', '.')] [('The', 'quick', 'brown'), ('quick', 'brown', 'foxes'), ('brown', 'foxes', 'jumped'), ('foxes', 'jumped', 'over'), ('jumped', 'over', 'the'), ('over', 'the', 'lazy'), ('the', 'lazy', 'dogs'), ('lazy', 'dogs', '.')]

In this example, bigrams and trigrams are generated from the text, which can be used to identify patterns and relationships between adjacent words or phrases in the data.

5. Part-of-speech (POS) tagging for NLP feature engineering

POS tagging involves labelling each word in a sentence with its corresponding part of speech, such as a noun, verb, or adjective. This information can be used to build more advanced features for the model.

Advantages

  • POS tagging can help improve the accuracy of text analysis by providing information about the syntactic structure of the text.
  • POS tagging can identify patterns and relationships between different parts of speech in the text.
  • POS tagging can generate predictive models to help classify or predict text based on the part-of-speech tags.

Disadvantages

  • POS tagging may not work well for all types of text or all languages.
  • POS tagging may not accurately capture the text’s intended meaning, as some words can have multiple possible part-of-speech tags depending on the context and usage.
  • POS tagging can be computationally intensive and require more memory, especially for larger datasets.

Example

Here’s an example of POS tagging in Python using the NLTK library:

import nltk 

text = "The quick brown foxes jumped over the lazy dogs." 
tokens = nltk.word_tokenize(text) 
pos_tags = nltk.pos_tag(tokens) 
print(pos_tags)

Output:

[('The', 'DT'), ('quick', 'JJ'), ('brown', 'NN'), ('foxes', 'NNS'), ('jumped', 'VBD'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dogs', 'NNS'), ('.', '.')]

In this example, each word in the text is assigned a specific part-of-speech tag based on its context and usage. The resulting POS tags can identify patterns and relationships between different parts of speech in the text.

6. Named Entity Recognition (NER) for NLP feature engineering

NER involves identifying and categorizing named entities in text, such as people, organizations, and locations. This information can also be used to build more advanced features for the model.

Advantages

  • NER can help improve the accuracy of text analysis by identifying and extracting important information from text.
  • NER can identify patterns and relationships between named entities in the text.
  • NER can be used to generate predictive models that can help classify or predict text based on the named entities.

Disadvantages

  • NER may not work well for all types of text or all languages.
  • NER may not accurately identify all named entities in the text, especially if the named entities are misspelt or ambiguous.
  • NER can be computationally intensive and require more memory, especially for larger datasets.

Example

Here’s an example of NER in Python using the NLTK library:

import nltk 

text = "John Smith works at Google in New York City." 
tokens = nltk.word_tokenize(text) 
pos_tags = nltk.pos_tag(tokens) 
ner_tags = nltk.ne_chunk(pos_tags) 
print(ner_tags)

Output:

(S (PERSON John/NNP) (PERSON Smith/NNP) works/VBZ at/IN (ORGANIZATION Google/NNP) in/IN (GPE New/NNP York/NNP City/NNP) ./.)

In this example, named entities such as PERSON, ORGANIZATION, and GPE (geo-political entity) are identified and classified. The resulting NER tags can extract important information from the text and identify patterns and relationships between named entities.

7. TF-IDF

Term Frequency-Inverse Document Frequency (TF-IDF) is a technique that assigns weights to words based on their frequency in the document and the corpus. This can help the model identify important words or phrases in the text.

Advantages

  • TF-IDF can help identify important keywords and concepts in a document corpus.
  • TF-IDF can be used to measure the relevance of a document to a query or search term.
  • TF-IDF can be used to cluster similar documents based on the similarity of their content.

Disadvantages

  • TF-IDF may not work well for all types of text or all languages.
  • TF-IDF may not accurately capture the meaning of the text, as it only considers the frequency of words and does not consider the context or semantics of the words.
  • TF-IDF can be computationally intensive and require more memory, especially for larger datasets.

Example

Here’s an example of TF-IDF in Python using the scikit-learn library:

from sklearn.feature_extraction.text import TfidfVectorizer 

corpus = [ "The quick brown fox jumps over the lazy dog.", 
           "The quick brown foxes jump over the lazy dogs and cats.", 
           "The lazy dogs and cats watch the quick brown foxes jump over the moon."] 

tfidf_vectorizer = TfidfVectorizer() 
tfidf_matrix = tfidf_vectorizer.fit_transform(corpus) 
print(tfidf_matrix.toarray())

Output:

[[0. 0. 0. 0.51785612 0. 0. 0. 0. 0. 0. 0.68091856 0.51785612 0. ] 
[0. 0. 0. 0.46519584 0. 0. 0.59817854 0. 0. 0. 0. 0.46519584 0.59817854] 
[0.33682422 0.33682422 0.33682422 0.30794004 0.33682422 0.33682422 0. 0.33682422 0.33682422 0.33682422 0. 0.30794004 0. ]]

In this example, TF-IDF represents the importance of each word in a corpus of three documents. The resulting TF-IDF matrix can be used to identify important keywords and concepts in the corpus, measure the relevance of a document to a query or search term, and cluster similar documents based on the similarity of their content.

8. Word Embeddings

Word embeddings are a neural network-based technique that converts words into dense vectors that capture semantic meaning. Word embeddings can be used as input features for deep learning models.

Advantages

  • Word embeddings can capture the meaning and context of words, allowing for more accurate text analysis and prediction.
  • Word embeddings can be used to represent words in a more efficient and scalable way than traditional bag-of-words approaches.
  • Word embedding can be trained on large amounts of text data to capture subtle linguistic patterns and relationships.

Disadvantages

  • Word embedding may not work well for all types of text or all languages.
  • Word embedding may not capture all the nuances of meaning and context, as they are based on statistical patterns in the data and may not reflect the true semantic relationships between words.
  • Word embedding can be computationally intensive and require more memory, especially for larger datasets.

Example

Here’s an example of word embeddings in Python using the Gensim library:

from gensim.models import Word2Vec 

sentences = [ "The quick brown fox jumps over the lazy dog".split(), 
              "The lazy dog watches the quick brown fox".split(), 
              "The quick brown cat jumps over the lazy dog".split(), 
              "The lazy dog watches the quick brown cat".split() ] 

model = Word2Vec(sentences, min_count=1) 
print(model['quick'])

NLP feature engineering for social media data

NLP feature engineering for social media data presents some unique challenges due to the informal nature of the language used and the abundance of noise in the data. Here are some techniques that can be used specifically for social media data:

  1. Hashtags and mentions: Hashtags and mentions can provide valuable contextual information about the content of a post. Extracting and encoding these features can help the model better understand the topic or subject matter of the post.
  2. Emojis and emoticons: Emojis and emoticons can convey sentiment and emotion, and including them as features can help the model better capture these aspects of the text.
  3. Spelling correction: Social media data is often riffed with misspellings and abbreviations, and correcting these can improve the quality of the input data for the model.
  4. Slang and abbreviations: Social media data is also characterized by the frequent use of slang and abbreviations. Expanding these into their full forms can help the model better understand the meaning of the text.
  5. Sentiment analysis: Sentiment analysis can be used to extract information about the emotional tone of the post. This information can be used to build more advanced features, such as the proportion of positive or negative sentiment words in the post.
  6. User profiling: User profiling involves using information about the user who created the post, such as their age, gender, location, and interests, to understand their behaviour and preferences better. This can be useful for targeted advertising or personalized recommendations.

Conclusion

NLP feature engineering techniques such as tokenization, stop word removal, stemming and lemmatization, n-grams, POS tagging, Named Entity Recognition, TF-IDF, and Word Embeddings are essential for the processing and analyzing text data in natural language processing.

Each method has pros and cons, and which one to use depends on the specific use case and the kind of text data being analyzed.

For example, tokenization and stop-word removal are basic techniques that can help simplify text data and make it more manageable. Stemming and lemmatization can help reduce the number of words to analyze while still capturing their essence. N-grams can help capture context and relationships between words, while POS tagging and Named Entity Recognition can help identify the grammatical structure and named entities in text data. TF-IDF can help identify the most important words in a document or corpus, while Word Embeddings can capture more complex relationships between words.

Overall, NLP feature engineering is an important part of natural language processing, and it can help you get meaningful insights and information from text data.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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