Sentiment analysis is an example of applied Natural Language Processing (NLP). In this context, “sentiment” is almost interchangeable with text polarity. Text polarity is a measure from -1 to 1 of the sentiment of the text. The dictionary definition of sentiment is actually “one’s view or attitude towards something”, so this could include emotions fromContinue reading “Text Sentiment Analysis and How to Do it”
Category Archives: spaCy
What AI Keyword Extraction Is and How to Do It
Keyword extraction is an example of applied Natural Language Processing (NLP). NLP is the subfield of AI concerned with analyzing, understanding, and generating language. Keyword extraction is one of the basic techniques in NLP. The first step to keyword extraction is tokenization. After tokenizing a text, it’s a simple step to look through for aContinue reading “What AI Keyword Extraction Is and How to Do It”
NLP: Stop Words, When and Why to Use Them
There are 326 “Stop Words” by default in spaCy. What are stopwords (or stop words)? They’re common words that we don’t want to include in some of our analysis when we perform Natural Language Processing. These are words that generally don’t contribute anything to the meaning of the text. However, we can’t always remove stopwords.Continue reading “NLP: Stop Words, When and Why to Use Them”
Top 3 Ready-to-Use Python NLP Libraries for 2022
80-90% of business data is unstructured text data. The businesses that win will be the ones that find a way to analyze their text data. How can we analyze text data? Natural Language Processing. NLP is one of the most important sectors of AI. It may be the fastest growing subfield of AI in theContinue reading “Top 3 Ready-to-Use Python NLP Libraries for 2022”
What is Lemmatization and How can I do It?
Lemmatization is an important part of Natural Language Processing. Other NLP topics we’ve covered include Text Polarity, Named Entity Recognition, and Summarization. Lemmatization is the process of turning a word into its lemma. A lemma is the “canonical form” of a word. A lemma is usually the dictionary version of a word, it’s picked byContinue reading “What is Lemmatization and How can I do It?”
Build Your Own AI Text Summarizer in Python
For this example, we’re going to build a naive extractive text summarizer in 25 lines of Python. An extractive summary is a summary of a document that is directly extracted from the text. For more information on AI summaries, check out this article on What is AI Text Summarization and How Can I Use It?Continue reading “Build Your Own AI Text Summarizer in Python”
Natural Language Processing: What is Text Polarity?
Natural Language Processing (NLP) and all of its applications will be huge in the 2020s. A lot of my blogging is about text processing and all the things that go with it such as Named Entity Recognition and Part of Speech Tagging. Text polarity is a basic text processing technique that gives us insight intoContinue reading “Natural Language Processing: What is Text Polarity?”
The Best Way to do Named Entity Recognition (NER)
Named Entity Recognition (NER) is a common Natural Language Processing technique. It’s so often used that it comes in the basic pipeline for spaCy. NER can help us quickly parse out a document for all the named entities of many different types. For example, if we’re reading an article, we can use named entity recognitionContinue reading “The Best Way to do Named Entity Recognition (NER)”
Natural Language Processing: Part of Speech Tagging
Part of Speech (POS) Tagging is an integral part of Natural Language Processing (NLP). The first step in most state of the art NLP pipelines is tokenization. Tokenization is the separating of text into “tokens”. Tokens are generally regarded as individual pieces of languages – words, whitespace, and punctuation. Once we tokenize our text weContinue reading “Natural Language Processing: Part of Speech Tagging”