A Quick Guide to Part of Speech Tagging

Leverage this essential building block of NLP to perform a Voice of Customer analysis

A Quick Guide to Part of Speech Tagging

Photo by Alexandre Pellaes on Unsplash

What is a Part of Speech?

Part of Speech (POS) is a way to describe the grammatical function of a word. In Natural Language Processing (NLP), POS is an essential building block of language models and interpreting text. While POS tags are used in higher-level functions of NLP, it’s important to understand them on their own, and it’s possible to leverage them for useful purposes in your text analysis.

There are eight (sometimes nine ) different parts of speech in English that are commonly defined. The Butte College introduces the following definitions:

Noun: A noun is the name of a person, place, thing, or idea.
Pronoun: A pronoun is a word used in place of a noun.
Verb: A verb expresses action or being.
Adjective: An adjective modifies or describes a noun or pronoun.
Adverb: An adverb modifies or describes a verb, an adjective, or another adverb.
Preposition: A preposition is a word placed before a noun or pronoun to form a phrase modifying another word in the sentence.
Conjunction: A conjunction joins words, phrases, or clauses.
Interjection: An interjection is a word used to express emotion.
Determiner or Article: A grammatical marker of definiteness (the) or indefiniteness (a, an). These are not always considered POS but are often included in POS tagging libraries.

The Basics of POS Tagging

Let’s start with some simple examples of POS tagging with three common Python libraries: NLTK, TextBlob, and Spacy. We’ll do the absolute basics for each and compare the results.

Start by importing all the needed libraries.

import nltk

from textblob import TextBlob
from textblob.taggers import PatternTagger

import spacy

For our examples, we’ll use two sentences with a common word, book, which can be a noun or a verb, to test how well the POS taggers work in context.

  • Please book my flight to California

  • I read a very good book


Let’s start with the most common library for NLP in Python; the Natural Language Toolkit or NLTK.

tokenized_sent = nltk.sent_tokenize("Please book my flight to California")
[nltk.pos_tag(nltk.word_tokenize(word)) for word in tokenized_sent]

[[('Please', 'NNP'),
('book', 'NN'),
('my', 'PRP$'),
('flight', 'NN'),
('to', 'TO'),
('California', 'NNP')]]

tokenized_sent = nltk.sent_tokenize("I read a very good book")
[nltk.pos_tag(nltk.word_tokenize(word)) for word in tokenized_sent]

[[('I', 'PRP'),
('read', 'VBP'),
('a', 'DT'),
('very', 'RB'),
('good', 'JJ'),
('book', 'NN')]]

What we notice here is that for the most part, NLTK properly recognizes the word in context; however, a few errors such as Please is tagged as a Proper Noun (NNP) and book is tagged as a Noun (NN) in our first sentence when it should be a Verb (VB).

Note: For a list of what the tags mean, see Penn Treebank Project.


Let’s try another library called TextBlob which provides a simple API for diving into standard natural language processing (NLP) tasks . It’s a very good Pythonic implementation of an NLP library and simplifies some of the common NLP tasks. Much of what TextBlob does is wrap NLTK and other popular NLP libraries to make them easier to use.

blob = TextBlob("Please book my flight to California", pos_tagger=PatternTagger())

[('Please', 'VB'),
('book', 'NN'),
('my', 'PRP$'),
('flight', 'NN'),
('to', 'TO'),
('California', 'NNP')]

blob = TextBlob("I read a very good book", pos_tagger=PatternTagger())

[('I', 'PRP'),
('read', 'VB'),
('a', 'DT'),
('very', 'RB'),
('good', 'JJ'),
('book', 'NN')]

Notice the use of PatternTagger in the initialization of the Blob. The default is to use NLTK's tagger, yielding the same results as above. This allows us to try a different POS Tagger and check its performance. We can see that TextBlob correctly identifies Please as a Verb this time but still misses Book as a Verb in the first sentence.


Spacy is the most modern and advanced of the three of these. It’s incredibly robust for tons of NLP tasks and allows for customization if more power is needed. This is currently my favorite NLP library, and let’s check it out with our sentences.

doc = nlp("Please book my flight to California")
for token in doc:
print(token.text, token.pos_)

Please INTJ
book VERB
flight NOUN
to ADP
California PROPN

doc = nlp("I read a very good book")
for token in doc:
print(token.text, token.pos_)

read VERB
very ADV
good ADJ
book NOUN

We see here that Spacy correctly tagged all of our words, and it identified Please like an Interjection as opposed to a Verb, which is more accurate and also identified Book as a Verb in the first sentence.

Each of these libraries has its pros and cons. I believe you should start with NLTK to understand how it works, especially since it has so much robust support of different corpora. TextBlob is great when you want simplicity across several NLP tasks, and Spacy when you want one of the most robust NLP libraries around.

Check out this great Series NLTK with Python for Natural Language from PythonProgramming.net.

Voice of Customer Analysis with Parts of Speech

One of the most common tasks performed with NLP is analyzing customer feedback from various sources and determining what customers are talking about for your product or service. This type of analysis is called Voice of Customer Analysis or VOC.

There are many ways to perform a VOC analysis. From Sentiment Analysis to Topic Modeling, one method you can use is Part of Speech tagging to narrow what customers are talking about and how they talk about your products and services.

Text for your analysis can come from survey responses, support tickets, Facebook comments, Tweets, chat conversations, emails, call transcripts, and online reviews. Let’s say you have a collection of customer reviews. One of the things you might want to identify is all the products that people are talking about. You may have a perfect categorization of your products in a database, but what if you don’t at the granular level you need? For this example, we will use the dataset of Women’s E-Commerce Clothing Reviews on Kaggle.

After importing the dataset, we can create a new DataFrame of all the words and their POS tag. The function below takes each review and determines the POS tag for each word; an important distinction because we get the context of each word in the sentence, and as we saw above, this makes a big difference in which POS tag is associated.

def pos_tag(text):
df = pd.DataFrame(columns = ['WORD', 'POS'])
doc = nlp(text)
for token in doc:
df = df.append({'WORD': token.text, 'POS': token.pos_}, ignore_index=True)
return df

Next, we can run the function on a subset of the reviews. Since we’re using individual words, the quantity can be in the millions, and we most likely don’t need the entire dataset.

# Take a random sample of reviews
df2 = df.sample(10000, random_state=42).copy()
# Create an empty dataframe to store the results
df_pos = pd.DataFrame(columns = ['WORD', 'POS'])
# Iterate through the reviews and append each POS tag to the dataframe
df_pos = pos_tag(df2['Review Text'].to_string())

(144498, 2)

Next, we can group and count each of the POS tags to see the most frequent ones.

df_top_pos = df_pos.groupby('POS')['POS'].count().\

Image by Author

Great! We have lots of tags and words. However, these don’t quite tell us much on their own, giving us a view of the distribution of different tags. However, now we can use our tags to pull out words that might represent products vs. those that represent other words in our reviews. For this, we can filter for just the Nouns.

df_nn = df_pos[df_pos['POS'] == 'NOUN'].copy()
sort_values(['count'], ascending=False).head(10)

         WORD  count
667     dress   1779
2062      top   1176
1764    shirt    463
1971  sweater    437
453     color    383
1807     size    312
765    fabric    287
1922    store    274
1822    skirt    256
1416    pants    246

Look at that! We have the top 15 words used in this subset of reviews and most of them look like product categories. What if we now look at the top Adjectives for the same subset of reviews?

df_adj = df_pos[df_pos['POS'] == 'ADJ'].copy()
sort_values(['count'], ascending=False).head(15)

            WORD  count
400        great    481
144    beautiful    405
248         cute    398
784         soft    321
218  comfortable    272
632      perfect    243
585         nice    196
776        small    176
41         Great    149
666       pretty    146
394     gorgeous    144
511       little    142
611        other    142
523       lovely    125
349   flattering    122

Tada 🎉! the top-most used word that describes how our customers are talking about our products. We have lots of positive words but also some words that might be worth looking into. Things such as small and little speak potentially to a sizing issue where some clothes are not true to fit. A product manager can take this information and dive deeper into the reviews which mention this word.

As mentioned, there are other ways to analyze text which might be better, such as Sentiment Analysis and Topic Modeling. However, this is a fun way to apply POS tagging in a practical use case and even be combined with those other NLP tools to help you get the most out of your customer’s feedback.

All the code for this analysis is available on GitHub.


Part of Speech on Wikipedia

What are the applications of tagging in NLP?

The Eight Parts of Speech