{"id":156978,"date":"2022-09-14T11:20:00","date_gmt":"2022-09-14T15:20:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=156978"},"modified":"2022-11-21T09:58:12","modified_gmt":"2022-11-21T14:58:12","slug":"natural-language-processing-in-python-using-spacy-part-ii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/natural-language-processing-in-python-using-spacy-part-ii\/","title":{"rendered":"Natural Language Processing in Python using spaCy \u2013 Part II"},"content":{"rendered":"\n

Part I<\/a> in this series provides and overview of spaCy and demonstrates how to install the library.<\/em><\/p>\n\n\n\n

Split text into sentences using spaCy<\/h3>\n\n\n\n
# Initialize the text that you want to analyze\ntext = \"Ripple was founded in 2012 by Jed McCaleb and Chris Larsen (co-founder of E-Loan). But the gears were in motion back in 2004, four years before Bitcoin, when a Canadian programmer, Ryan Fugger, developed RipplePay.RipplePay, though not based on blockchain, was a secure payment system for a financial network. In 2012, Jed McCaleb and Chris Larsen started the company OpenCoin, renamed Ripple Labs in 2013.\"\ntext\n# Source: https:\/\/blog.quantinsti.com\/ripple-xrp\/<\/code><\/pre>\n\n\n\n
show_text.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Source: Exploring Ripple and XRP: What it is, Features, and More<\/a><\/code><\/pre>\n\n\n\n
Let us now pass this text to our model and split it into individual sentences.<\/p>\n\n\n\n
doc = nlp(text)\n\n# Split the text into sentences\nsentences = [sentence.text for sentence in doc.sents]\n# Print the original text\nprint(f\"Original text: \\n\\n{text}\\n{'-'*50}\\n\")\n# Print the different sentences from the text\nprint(\n    f\"We have split the above into {len(sentences)} sentences:\\n\\n 1. {sentences[0]}\\n2. {sentences[1]}\")<\/code><\/pre>\n\n\n\n
split_the_text.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Removing punctuation using spaCy<\/h2>\n\n\n\n
Before we proceed further with our analysis, let us remove the punctuations from our text.<\/p>\n\n\n\n
token_without_punc = [token for token in doc if not token.is_punct]\ntoken_without_punc<\/code><\/pre>\n\n\n\n
remove_punctuation.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Removing stop words using spaCy<\/h2>\n\n\n\n
We can remove the stop words like and<\/em>, the<\/em>, etc. from the text because these words don\u2019t add much value to it for analysis purposes.<\/p>\n\n\n\n
For this, we first get the list of all the stop words for that language. There are two ways in which we can do it.<\/p>\n\n\n\n
# Get the stop words: 1st method\nall_stopwords = nlp.Defaults.stop_words\n\nlen(all_stopwords) # Output: 326<\/code><\/pre>\n\n\n\n
remove_stop_words.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
This is the second method.<\/p>\n\n\n\n
# Get the stop words: 2nd method\nfrom spacy.lang.en.stop_words import STOP_WORDS\n\nlen(STOP_WORDS) # Output: 326<\/code><\/pre>\n\n\n\n
remov_stop_words_2nd_method.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
As we can see from the code below, the results of both the above methods are the same.<\/p>\n\n\n\n
# Both are the same\nall_stopwords is STOP_WORDS<\/code><\/pre>\n\n\n\n
check_methods_equality.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Now that we have got the list of stop words, let\u2019s remove them from our text.<\/p>\n\n\n\n
# Remove the stop words\ntoken_without_stop = [\n    token for token in token_without_punc if not token.is_stop]\ntoken_without_stop<\/code><\/pre>\n\n\n\n
remove_stop_words_from_text.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
POS tagging using spaCy<\/h2>\n\n\n\n
Let us now see how spaCy tags each token with its respective part-of-speech.<\/p>\n\n\n\n
import pandas as pd\n\ntoken_pos = []\nfor token in token_without_punc:\n    token_pos.append([token.text, token.lemma_, token.pos_, token.tag_, token.dep_,\n                      token.shape_, token.is_alpha, token.is_stop])\ndf = pd.DataFrame(token_pos, columns=['Text', 'Lemma', 'POS', 'Tag',\n                  'Syntactic dependency relation', 'Shape', 'Is Alphabet', 'Is stop word'])\ndf<\/code><\/pre>\n\n\n\n
pos_tagging.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
If you would like to know more about the different token attributes, please click here<\/a>.<\/p>\n\n\n\n
Named entity recognition using spaCy<\/h2>\n\n\n\n
What is a named entity? It is any \u2018real-world object\u2019 with an assigned name. It may be a country, city, person, building, company, etc. spaCy models can predict the different named entities in a text with remarkable accuracy.<\/p>\n\n\n\n
ner = []\nfor ent in doc.ents:\n    ner.append([ent.text, ent.start_char, ent.end_char, ent.label_])\n\ndf_ner = pd.DataFrame(\n    ner, columns=['Text', 'Start character', 'End character', 'Label'])\ndf_ner<\/code><\/pre>\n\n\n\n
named_entity.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Dependency Visualization using displaCy<\/h2>\n\n\n\n
Adding another powerful functionality to it\u2019s arsenal, spaCy also comes with a built-in dependency visualizer valled displaCy<\/a> that lets you check your model’s predictions in your browser.<\/p>\n\n\n\n
To use it from the browser, we use the \u2018serve\u2019 method. DisplaCy can auto-detect if you are working on a Jupyter notebook. We can use the \u2018render\u2019 method to return markup that can be rendered in a cell.<\/p>\n\n\n\n
Let us try the spaCy\u2019s entity visualizer to render the NER results we got above.<\/p>\n\n\n\n
For this, let us import displaCy which is \u2018a modern and service-independent visualisation library<\/strong>\u2019<\/p>\n\n\n\n
from spacy import displacy\n\ndisplacy.render(doc, style=\"ent\")<\/code><\/pre>\n\n\n\n
dependency_visualisation.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Output:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Getting linguistic annotations using spaCy<\/h2>\n\n\n\n
You can get insights into the grammatical structure of a text using spaCy\u2019s linguistic annotations functionality. It basically tells you the part of speech each word belongs to and how the different words are related to each other.<\/p>\n\n\n\n
We need to have installed spaCy and the trained model that we want to use. In this blog, we\u2019ll work with the English language model, the en_core_web_sm.<\/p>\n\n\n\n
Let\u2019s look at an example.<\/p>\n\n\n\n
# Pass the text you want to analyze to your model\ndoc = nlp(\"Jennifer is learning quantitative analysis.\")\n\n# Print the part-of-speech and the syntactic dependency relation for the tokens\nfor token in doc:\n    print(token.text, token.pos_, token.dep_)<\/code><\/pre>\n\n\n\n
linguistic_annotations.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Let\u2019s visualize the syntactic relationship between the different tokens.<\/p>\n\n\n\n
from spacy import displacy\ndisplacy.render(doc, style=\"dep\")<\/code><\/pre>\n\n\n\n
visualise_syntactic_relationship.py <\/a>hosted with \u2764 by GitHub<\/a><\/code><\/pre>\n\n\n\n
Output:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
spaCy examples on Github<\/h2>\n\n\n\n
We\u2019ve covered some of the basic NLP techniques using the spaCy library. To delve into further details, you can head to the spaCy GitHub page<\/a> where you can find tons of examples to help you in your journey into the spaCy universe.<\/p>\n\n\n\n
In addition to the development techniques, you can also try out the extensive test suite<\/a> offered by spaCy which uses the pytest<\/a> framework.<\/p>\n\n\n\n
\n\n\n\n
Conclusion<\/h3>\n\n\n\n
We\u2019ve explored some of the main features of spaCy, but there is a lot more to be explored in this powerful Python natural language processing library. I hope that this blog has gotten you started with the initial few steps of this journey. So go ahead and explore the enormous world of human language and thoughts!<\/p>\n\n\n\n
Want to harness alternate sources of data, to quantify human sentiments expressed in news and tweets using machine learning techniques? Check out this course on Trading Strategies with News and Tweets<\/a>. You can use sentiment indicators and sentiment scores to create a trading strategy and implement the same in live trading.<\/p>\n\n\n\n
Till then, happy tweeting and Pythoning!<\/p>\n\n\n\n
Visit QuantInsti website for additional insight on this topic: https:\/\/blog.quantinsti.com\/spacy-python\/<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"
Adding another powerful functionality to it\u2019s arsenal, spaCy also comes with a built-in dependency visualizer valled\u00a0displaCy\u00a0that lets you check your model’s predictions in your 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