These are tutorials with code samples that teach you how to accomplish specific goals with text data. For example, extracting topics from text, using vectorizers correctly and etc.
Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts and more. The sky is the limit when it comes to how you can use these embeddings for …
Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI Read More »
Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use which. CountVectorizer vs. HashingVectorizer HashingVectorizer and CountVectorizer are meant to do the same thing. Which is to convert …
Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a highly flexible feature representation module for text. In this article, we are going to go in-depth into the …
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear from spammy emails! Other than spam detection, text classifiers can be used to determine sentiment in social media …
Build Your First Text Classifier in Python with Logistic Regression Read More »
What are pre-trained embeddings and why? Pre-trained word embeddings are vector representation of words trained on a large dataset. With pre-trained embeddings, you will essentially be using the weights and vocabulary from the end result of the training process done by….someone else! (It could also be you) One benefit of using pre-trained embeddings is that …
Easily Access Pre-trained Word Embeddings with Gensim Read More »
Scikit-learn’s Tfidftransformer and Tfidfvectorizer aim to do the same thing, which is to convert a collection of raw documents to a matrix of TF-IDF features. The differences between the two modules can be quite confusing and it’s hard to know when to use which. This article shows you how to correctly use each module, the …
About a year ago, I looked high and low for a python word cloud library that I could use from within my Jupyter notebook that was flexible enough to use counts or tfidf when needed or just accept a set of words and corresponding weights. I was a bit surprised that something like that did …
Python Word Cloud for Jupyter Notebooks and Web Apps Read More »
In this era of use Deep Learning for everything, one may be wondering why you would even use TF-IDF for any task at all ?!! The truth is TF-IDF is easy to understand, easy to compute and is one of the most versatile statistic that shows the relative importance of a word or phrase in …
Training a Word2Vec model with phrases is very similar to training a Word2Vec model with single words. The difference: you would need to add a layer of intelligence in processing your text data to pre-discover phrases. In this tutorial, you will learn how to create embeddings with phrases without explicitly specifying the number of words …
How to incorporate phrases into Word2Vec – a text mining approach Read More »
ROUGE 2.0 is an easy to use evaluation toolkit for Automatic Summarization tasks. It uses the ROUGE system of metrics which works by comparing an automatically produced summary or translation against a set of reference summaries (typically human-produced). ROUGE is one of the standard ways to compute effectiveness of auto generated summaries. To understand how ROUGE works …