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An Introduction To Natural Language Processing With Python For SEOs
Natural language processing (NLP) is becoming more important than ever for SEO professionals.
It is crucial to start building the skills that will prepare you for all the amazing changes happening around us.
Hopefully, this column will motivate you to get started!
We are going to learn practical NLP while building a simple knowledge graph from scratch.
As Google, Bing, and other search engines use Knowledge Graphs to encode knowledge and enrich search results, what better way to learn about them than to build one?
Specifically, we are going to extract useful facts automatically from Search Engine Journal XML sitemaps.
In order to do this and keep things simple and fast, we will pull article headlines from the URLs in the XML sitemaps.
We will extract named entities and their relationships from the headlines.
Finally, we will build a powerful knowledge graph and visualize the most popular relationships.
In the example below the relationship is "launches."
The way to read the graph is to follow the direction of the arrows: subject "launches" object.
For example:
These facts and over a thousand more were extracted and grouped automatically!
Let's get in on the fun.
Here is the technical plan:
I recently had an enlightening conversation with Elias Dabbas from The Media Supermarket and learned about his wonderful Python library for marketers: advertools.
Some of my old Search Engine Journal articles are not working with the newer library versions. He gave me a good idea.
If I print the versions of third-party libraries now, it would be easy to get the code to work in the future.
I would just need to install the versions that worked when they fail. 🤓
%%capture !Pip install advertools import advertools as adv print(adv.__version__) #0.10.6We are going to download all Search Engine Journal sitemaps to a pandas data frame with two lines of code.
sitemap_url = "https://www.Searchenginejournal.Com/sitemap_index.Xml" df= adv.Sitemap_to_df(sitemap_url)
One cool feature in the package is that it downloaded all the linked sitemaps in the index and we get a nice data frame.
Look how simple it is to filter articles/pages from this year. We have 1,550 articles.
df[df["lastmod"] > '2020-01-01']
The advertools library has a function to break URLs within the data frame, but let's do it manually to get familiar with the process.
from urllib.Parse import urlparse import re example_url="https://www.Searchenginejournal.Com/google-be-careful-relying-on-3rd-parties-to-render-website-content/376547/" u = urlparse(example_url) print(u) #output -> ParseResult(scheme='https', netloc='www.Searchenginejournal.Com', path='/google-be-careful-relying-on-3rd-parties-to-render-website-content/376547/', params='', query='', fragment='')Here we get a named tuple, ParseResult, with a breakdown of the URL components.
We are interested in the path.
We are going to use a simple regex to split it by / and – characters
slug = re.Split("[/-]", u.Path) print(slug) #output ['', 'google', 'be', 'careful', 'relying', 'on', '3rd', 'parties', 'to', 'render', 'website', 'content', '376547', '']Next, we can convert it back to a string.
headline = " ".Join(slug) print(headline)#output
' google be careful relying on 3rd parties to render website content 376547 'The slugs contain a page identifier that is useless for us. We will remove with a regex.
headline = re.Sub("\d{6}", "",headline) print(headline) #output ' google be careful relying on 3rd parties to render website content ' #Strip whitespace at the borders headline = headline.Strip() print(headline) #output 'google be careful relying on 3rd parties to render website content'Now that we tested this, we can convert this code to a function and create a new column in our data frame.
def get_headline(url): u = urlparse(url) if len(u.Path) > 1: slug = re.Split("[/-]", u.Path) new_headline = re.Sub("\d{6}", ""," ".Join(slug)).Strip() #skip author and category pages if not re.Match("authorcategory", new_headline): return new_headline return ""Let's create a new column named headline.
new_df["headline"] = new_df["url"].Apply(lambda x: get_headline(x))
Let's explore and visualize the entities in our headlines corpus.
First, we combine them into a single text document.
import spacy from spacy import displacy text = "\n".Join([x for x in new_df["headline"].Tolist() if len(x) > 0]) nlp = spacy.Load("en_core_web_sm") doc = nlp(text) displacy.Render(doc, style="ent", jupyter=True)
We can see some entities correctly labeled and some incorrectly labeled like Hulu as a person.
There are also several missed like Facebook and Google Display Network.
spaCy's out of the box NER is not perfect and generally needs training with custom data to improve detection, but this is good enough to illustrate the concepts in this tutorial.
Building a Knowledge GraphNow, we get to the exciting part.
Let's start by evaluating the grammatical relationships between the words in each sentence.
We do this by printing the syntactic dependency of the entities.
for tok in doc[:100]: print(tok.Text, "...", tok.Dep_)
We are looking for subjects and objects connected by a relationship.
We will use spaCy's rule-based parser to extract subjects and objects from the headlines.
The rule can be something like this:
Extract the subject/object along with its modifiers, compound words and also extract the punctuation marks between them.
Let's first import the libraries that we will need.
from spacy.Matcher import Matcher from spacy.Tokens import Span import networkx as nx import matplotlib.Pyplot as plt from tqdm import tqdmTo build a knowledge graph, the most important things are the nodes and the edges between them.
The main idea is to go through each sentence and build two lists. One with the entity pairs and another with the corresponding relationships.
We are going to borrow a couple of functions created by Data Scientist, Prateek Joshi.
Let's test them on 100 sentences and see what the output looks like. I added len(x) > 0 to skip empty lines.
for t in [x for x in new_df["headline"].Tolist() if len(x) > 0][:100]: print(get_entities(t))
Many extractions are missing elements or are not great, but as we have so many headlines, we should be able to extract useful facts anyways.
Now, let's build the graph.
entity_pairs = [] for i in tqdm([x for x in new_df["headline"].Tolist() if len(x) > 0]): entity_pairs.Append(get_entities(i))Here are some example pairs.
entity_pairs[10:20] #output [['chrome', ''], ['google assistant', '500 million 500 users'], ['', ''], ['seo metrics', 'how them'], ['google optimization', ''], ['twitter', 'new explore tab'], ['b2b', 'greg finn podcast'], ['instagram user growth', 'lower levels'], ['', ''], ['', 'advertiser']]Next, let's build the corresponding relationships. Our hypothesis is that the predicate is actually the main verb in a sentence.
relations = [get_relation(i) for i in tqdm([x for x in new_df["headline"].Tolist() if len(x) > 0])] print(relations[10:20]) #output ['blocker', 'has', 'conversions', 'reports', 'ppc', 'rolls', 'paid', 'drops to lower', 'marketers', 'facebook']Next, let's rank the relationships.
pd.Series(relations).Value_counts()[4:50]
Finally, let's build the knowledge graph.
# extract subject source = [i[0] for i in entity_pairs] # extract object target = [i[1] for i in entity_pairs] kg_df = pd.DataFrame({'source':source, 'target':target, 'edge':relations}) # create a directed-graph from a dataframe G=nx.From_pandas_edgelist(kg_df, "source", "target", edge_attr=True, create_using=nx.MultiDiGraph()) plt.Figure(figsize=(12,12)) pos = nx.Spring_layout(G) nx.Draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.Cm.Blues, pos = pos) plt.Show()This plots a monster graph, which, while impressive, is not particularly useful.
Let's try again, but take only one relationship at a time.
In order to do this, we will create a function that we pass the relationship text as input (bolded text).
def display_graph(relation): G=nx.From_pandas_edgelist(kg_df[kg_df['edge']==relation], "source", "target", edge_attr=True, create_using=nx.MultiDiGraph()) plt.Figure(figsize=(12,12)) pos = nx.Spring_layout(G, k = 0.5) # k regulates the distance between nodes nx.Draw(G, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.Cm.Blues, pos = pos) plt.Show()Now, when I run display_graph("launches"), I get the graph at the beginning of the article.
Here are a few more relationships that I plotted.
I created a Colab notebook with all the steps in this article and at the end, you will find a nice form with many more relationships to check out.
Just run all the code, click on the pulldown selector and click on the play button to see the graph.
Here are some resources that I found useful while putting this tutorial together.
I asked my follower to share the Python projects and excited to see how many creative ideas coming to life from the community! 🐍🔥
More Resources:
Image Credits
All screenshots taken by author, August 2020
8 Great Python Libraries For Natural Language Processing
With so many NLP resources in Python, how to choose? Discover the best Python libraries for analyzing text and how to use them.
Natural language processing, or NLP for short, is best described as "AI for speech and text." The magic behind voice commands, speech and text translation, sentiment analysis, text summarization, and many other linguistic applications and analyses, natural language processing has been improved dramatically through deep learning.
The Python language provides a convenient front-end to all varieties of machine learning including NLP. In fact, there is an embarrassment of NLP riches to choose from in the Python ecosystem. In this article we'll explore each of the NLP libraries available for Python—their use cases, their strengths, their weaknesses, and their general level of popularity.
Note that some of these libraries provide higher-level versions of the same functionality exposed by others, making that functionality easier to use at the cost of some precision or performance. You'll want to choose a library well-suited both to your level of expertise and to the nature of the project.
CoreNLPThe CoreNLP library — a product of Stanford University — was built to be a production-ready natural language processing solution, capable of delivering NLP predictions and analyses at scale. CoreNLP is written in Java, but multiple Python packages and APIs are available for it, including a native Python NLP library called Stanza.
CoreNLP includes a broad range of language tools—grammar tagging, named entity recognition, parsing, sentiment analysis, and plenty more. It was designed to be human language agnostic, and currently supports Arabic, Chinese, French, German, and Spanish in addition to English (with Russian, Swedish, and Danish support available from third parties). CoreNLP also includes a web API server, a convenient way to serve predictions without too much additional work.
The easiest place to start with CoreNLP's Python wrappers is Stanza, the reference implementation created by the Stanford NLP Group. In addition to being well-documented, Stanza is also maintained regularly; many of the other Python libraries for CoreNLP were not updated for some time.
CoreNLP also supports the use of NLTK, a major Python NLP library discussed below. As of version 3.2.3, NLTK includes interfaces to CoreNLP in its parser. Just be sure to use the correct API.
The obvious downside of CoreNLP is that you'll need some familiarity with Java to get it up and running, but that's nothing a careful reading of the documentation can't achieve. Another hurdle could be CoreNLP's licensing. The whole toolkit is licensed under the GPLv3, meaning any use in proprietary software that you distribute to others will require a commercial license.
GensimGensim does just two things, but does them exceedingly well. Its focus is statistical semantics—analyzing documents for their structure, then scoring other documents based on their similarity.
Gensim can work with very large bodies of text by streaming documents to its analysis engine and performing unsupervised learning on them incrementally. It can create multiple types of models, each suited to different scenarios: Word2Vec, Doc2Vec, FastText, and Latent Dirichlet Allocation.
Gensim's detailed documentation includes tutorials and how-to guides that explain key concepts and illustrate them with hands-on examples. Common recipes are also available on the Gensim GitHub repo.
The latest version, Gensim 4, supports Python 3 only but brings major optimizations to common algorithms such as Word2Vec, a less complex OOP model, and many other modernizations.
NLTKThe Natural Language Toolkit, or NLTK for short, is among the best-known and most powerful of the Python natural language processing libraries. Many corpora (data sets) and trained models are available to use with NLTK out of the box, so you can start experimenting with NLTK right away.
As the documentation states, NLTK provides a wide variety of tools for working with text: "classification, tokenization, stemming, tagging, parsing, and semantic reasoning." It can also work with some third-party tools to enhance its functionality, such as the Stanford Tagger, TADM, and MEGAM.
Keep in mind that NLTK was created by and for an academic research audience. It was not designed to serve NLP models in a production environment. The documentation is also somewhat sparse; even the how-tos are thin. Also, there is no 64-bit binary; you'll need to install the 32-bit edition of Python to use it. Finally, NLTK is not the fastest library either, but it can be sped up with parallel processing.
If you are determined to leverage what's inside NLTK, you might start instead with TextBlob (discussed below).
PatternIf all you need to do is scrape a popular website and analyze what you find, reach for Pattern. This natural language processing library is far smaller and narrower than other libraries covered here, but that also means it's focused on doing one common job really well.
Pattern comes with built-ins for scraping a number of popular web services and sources (Google, Wikipedia, Twitter, Facebook, generic RSS, etc.), all of which are available as Python modules (e.G., from pattern.Web import Twitter). You don't have to reinvent the wheels for getting data from those sites, with all of their individual quirks. You can then perform a variety of common NLP operations on the data, such as sentiment analysis.
Pattern exposes some of its lower-level functionality, allowing you to to use NLP functions, n-gram search, vectors, and graphs directly if you like. It also has a built-in helper library for working with common databases (MySQL, SQLite, and MongoDB in the future), making it easy to work with tabular data stored from previous sessions or obtained from third parties.
PolyglotPolyglot, as the name implies, enables natural language processing applications that deal with multiple languages at once.
The NLP features in Polyglot echo what's found in other NLP libraries: tokenization, named entity recognition, part-of-speech tagging, sentiment analysis, word embeddings, etc. For each of these operations, Polyglot provides models that work with the needed languages.
Note that Polyglot's language support differs greatly from feature to feature. For instance, the language detection system supports almost 200 languages, tokenization supports 165 languages (largely because it uses the Unicode Text Segmentation algorithm), and sentiment analysis supports 136 languages, while part-of-speech tagging supports only 16.
PyNLPIPyNLPI (pronounced "pineapple") has only a basic roster of natural language processing functions, but it has some truly useful data-conversion and data-processsing features for NLP data formats.
Most of the NLP functions in PyNLPI are for basic jobs like tokenization or n-gram extraction, along with some statistical functions useful in NLP like Levenshtein distance between strings or Markov chains. Those functions are implemented in pure Python for convenience, so they're unlikely to have production-level performance.
But PyNLPI shines for working with some of the more exotic data types and formats that have sprung up in the NLP space. PyNLPI can read and process GIZA, Moses++, SoNaR, Taggerdata, and TiMBL data formats, and devotes an entire module to working with FoLiA, the XML document format used to annotate language resources like corpora (bodies of text used for translation or other analysis).
You'll want to reach for PyNLPI whenever you're dealing with those data types.
SpaCySpaCy, which taps Python for convenience and Cython for speed, is billed as "industrial-strength natural language processing." Its creators claim it compares favorably to NLTK, CoreNLP, and other competitors in terms of speed, model size, and accuracy. SpaCy contains models for multiple languages, although only 16 of the 64 supported have full data pipelines available for them.
SpaCy includes most every feature found in those competing frameworks: speech tagging, dependency parsing, named entity recognition, tokenization, sentence segmentation, rule-based match operations, word vectors, and tons more. SpaCy also includes optimizations for GPU operations—both for accelerating computation, and for storing data on the GPU to avoid copying.
The documentation for SpaCy is excellent. A setup wizard generates command-line installation actions for Windows, Linux, and macOS and for different Python environments (pip, conda, etc.) as well. Language models install as Python packages, so they can be tracked as part of an application's dependency list.
The latest version of the framework, SpaCy 3.0, provides many upgrades. In addition to using the Ray framework for performing distributed training on multiple machines, it offers a new transformer-based pipeline system for better accuracy, a new training system and workflow configuration model, end-to-end workflow managament, and a good deal more.
TextBlobTextBlob is a friendly front-end to the Pattern and NLTK libraries, wrapping both of those libraries in high-level, easy-to-use interfaces. With TextBlob, you spend less time struggling with the intricacies of Pattern and NLTK and more time getting results.
TextBlob smooths the way by leveraging native Python objects and syntax. The quickstart examples show how texts to be processed are simply treated as strings, and common NLP methods like part-of-speech tagging are available as methods on those string objects.
Another advantage of TextBlob is you can "lift the hood" and alter its functionality as you grow more confident. Many default components, like the sentiment analysis system or the tokenizer, can be swapped out as needed. You can also create high-level objects that combine components—this sentiment analyzer, that classifier, etc.—and re-use them with minimal effort. This way, you can prototype something quickly with TextBlob, then refine it later.
Top 10 Best Python Libraries For Sentiment Analysis In 2025
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Python is a popular programming language extensively used in various applications including Natural Language Processing (NLP). Sentiment analysis, a frequent NLP task, aids in understanding the underlying emotion or sentiment in a given text. For this purpose, Python offers a selection of libraries each possessing unique features and capabilities specially designed for sentiment analysis.
One of the top Python libraries for sentiment analysis is Pattern, which is a multipurpose library that can handle NLP, data mining, network analysis, machine learning, and visualization. Another popular library is TextBlob, which simplifies the process of sentiment analysis and offers an intuitive API and a host of NLP capabilities. The Natural Language Toolkit (NLTK) is also a widely used library that contains various utilities for manipulating and analyzing linguistic data, including text classifiers that can be used for sentiment analysis. These libraries, along with others, can be used to perform sentiment analysis on a wide range of text data, including social media posts, product reviews, and news articles.
Understanding Sentiment AnalysisSentiment analysis is a process of identifying and categorizing opinions expressed in a piece of text. It is a subfield of Natural Language Processing (NLP) that uses machine learning algorithms to determine the sentiment of a text, whether it is positive, negative, or neutral.
Sentiment analysis is widely used in various industries, including marketing, finance, politics, and customer service. It enables companies to understand the opinions and emotions of their customers, which can help them make better decisions and improve their products and services.
There are two main approaches to sentiment analysis: rule-based and machine learning-based. Rule-based methods use pre-defined rules and lexicons to determine the sentiment of a text, while machine learning-based methods use algorithms to learn from data and identify patterns in the text.
Python has several libraries that can be used for sentiment analysis, including Pattern, NLTK, TextBlob, and spaCy. These libraries provide a wide range of features, such as tokenization, part-of-speech tagging, and sentiment analysis.
Sentiment analysis can be challenging due to the complexity and variability of human language. Text can be ambiguous, sarcastic, or contain slang, which can affect the accuracy of sentiment analysis. However, with the help of machine learning algorithms and advanced NLP techniques, sentiment analysis can be a valuable tool for businesses to gain insights into their customers' opinions and emotions.
Why Python for Sentiment AnalysisPython is a powerful and versatile programming language that is widely used in many fields, including data science, machine learning, and natural language processing (NLP). Python provides a rich set of libraries and tools that make it easy to perform sentiment analysis tasks, even for those with little or no experience in programming.
Python is an ideal language for sentiment analysis because it offers a wide range of libraries and tools that can be used to perform text analysis tasks. Python libraries such as Pattern, BERT, TextBlob, spaCy, CoreNLP, scikit-learn, Polyglot, PyTorch, and Flair are some of the best libraries available for sentiment analysis. Each library has its strengths and weaknesses, and choosing the right library depends on the specific needs of the project.
1. PatternPattern is a Python library that provides tools for sentiment analysis, part-of-speech tagging, and other natural language processing tasks. Pattern is easy to use and provides a simple interface for performing sentiment analysis tasks.
3. BERTBERT (Bidirectional Encoder Representations from Transformers) is a powerful language model developed by Google. BERT is widely used for natural language processing tasks such as sentiment analysis. BERT is pre-trained on large amounts of text data and can be fine-tuned for specific tasks, making it a powerful tool for sentiment analysis.
4. TextBlobTextBlob is a Python library that provides tools for sentiment analysis, part-of-speech tagging, and other natural language processing tasks. TextBlob is easy to use and provides a simple interface for performing sentiment analysis tasks.
5. SpaCyspaCy is a Python library that provides tools for natural language processing tasks such as part-of-speech tagging, named entity recognition, and dependency parsing. SpaCy also provides tools for sentiment analysis, making it a powerful tool for sentiment analysis tasks.
6. CoreNLPCoreNLP is a Java library developed by Stanford University that provides tools for natural language processing tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. CoreNLP can be used in Python through the Py4J library, making it a powerful tool for sentiment analysis tasks.
7. Scikit-learnscikit-learn is a Python library that provides tools for machine learning tasks such as classification, regression, and clustering. Scikit-learn also provides tools for sentiment analysis, making it a powerful tool for sentiment analysis tasks.
8. PolyglotPolyglot is a Python library that provides tools for natural language processing tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Polyglot supports over 130 languages, making it a powerful tool for sentiment analysis tasks that involve multiple languages.
9. PyTorchPyTorch is a Python library developed by Facebook that provides tools for machine learning tasks such as deep learning and neural networks. PyTorch also provides tools for sentiment analysis, making it a powerful tool for sentiment analysis tasks.
10. FlairFlair is a Python library developed by Zalando Research that provides tools for natural language processing tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Flair uses state-of-the-art deep learning models for sentiment analysis, making it a powerful tool for sentiment analysis tasks.
Overall, Python is an ideal language for sentiment analysis because it provides a wide range of libraries and tools that can be used to perform text analysis tasks. Choosing the right library depends on the specific needs of the project.
Choosing the Right LibraryWhen it comes to sentiment analysis, choosing the right Python library can make all the difference. With so many options available, it can be difficult to know where to start. Here are a few things to consider when selecting a library for your project:
AccuracyOne of the most important factors to consider is the accuracy of the library. Some libraries may be better suited for certain types of data or languages, so it's important to test them thoroughly before making a final decision.
Ease of UseAnother important factor to consider is the ease of use of the library. Some libraries may require more setup or configuration than others, so it's important to choose a library that fits your skill level and time constraints.
SpeedDepending on the size of your dataset, the speed of the library may also be a factor to consider. Some libraries may be faster than others, so it's important to test them with your specific dataset to ensure they can handle the workload.
FeaturesFinally, consider the features offered by the library. Some libraries may offer more advanced features, such as sentiment analysis for specific industries or sentiment analysis for social media data. It's important to choose a library that offers the features you need for your specific project.
Overall, choosing the right Python library for sentiment analysis requires careful consideration of accuracy, ease of use, speed, and features. By taking the time to evaluate your options and test them with your specific dataset, you can ensure you choose the right library for your project.
ConclusionIn conclusion, sentiment analysis is a crucial aspect of natural language processing, and Python offers a wide range of powerful libraries for this task. Each library has its own advantages and disadvantages, and the choice of library depends on the specific needs of the project.
Pattern is a versatile Python library that can handle various NLP tasks, including sentiment analysis. NLTK is a popular library that offers a wide range of tools for text analysis, including sentiment analysis. TextBlob is an easy-to-use library that provides a simple API for sentiment analysis. VADER is a rule-based library that is specifically designed for sentiment analysis of social media texts. SpaCy is a fast and efficient library that can handle large volumes of text data.
Other libraries, such as Gensim, Scikit-learn, and TensorFlow, can also be used for sentiment analysis, depending on the specific requirements of the project. It is important to carefully evaluate the strengths and weaknesses of each library before making a choice.
Overall, Python offers a rich ecosystem of libraries for sentiment analysis, and developers can choose the best tool for their specific needs. By leveraging the power of these libraries, developers can build robust and accurate sentiment analysis models that can be used in a wide range of applications, from social media monitoring to market research to customer feedback analysis.
Frequently Asked Questions What are some popular Python libraries for sentiment analysis?Python has a wide range of libraries for sentiment analysis. Some of the popular ones include TextBlob, VADER, Pattern, spaCy, Scikit-learn, and NLTK. These libraries offer various features such as sentiment analysis, text classification, and entity recognition.
How does VADER perform in sentiment analysis compared to other Python libraries?VADER (Valence Aware Dictionary and sEntiment Reasoner) is a rule-based sentiment analysis tool that is specifically designed for social media texts. VADER outperforms other sentiment analysis libraries in terms of accuracy and speed for social media texts. However, it may not perform well for other types of texts.
What are the advantages of using TextBlob for sentiment analysis in Python?TextBlob is a simple and easy-to-use library for sentiment analysis in Python. It has a built-in sentiment analyzer that uses a machine learning algorithm to classify text as positive, negative, or neutral. TextBlob also offers other features such as part-of-speech tagging and noun phrase extraction.
What is spaCy's approach to sentiment analysis and how does it compare to other libraries?spaCy is a popular library for natural language processing in Python. Its approach to sentiment analysis is based on machine learning algorithms. SpaCy's sentiment analysis model is trained on a large dataset of movie reviews and can classify text as positive, negative, or neutral. Compared to other libraries, spaCy is known for its speed and performance.
How does BERT perform in sentiment analysis tasks using Python?BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can be fine-tuned for various natural language processing tasks, including sentiment analysis. BERT has shown promising results in sentiment analysis tasks and has outperformed other state-of-the-art models.
Which Python library is better for sentiment analysis – Scikit-learn or TextBlob?Scikit-learn is a popular machine learning library in Python that offers various algorithms for text classification and sentiment analysis. TextBlob, on the other hand, is a simpler library that is easier to use for sentiment analysis tasks. The choice between the two libraries depends on the specific requirements of the project and the user's familiarity with the libraries.
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