N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). For example, for the sentence “The cow jumps over the moon”. If N=2 (known as bigrams), then the ngrams would be: the cow cow jumps jumps over over the the moon So […]

Term Frequency (TF) Term frequency (TF) often used in Text Mining, NLP and Information Retrieval tells you how frequently a term occurs in a document. In the context natural language, terms correspond to words or phrases. Since every document is different in length, it is possible that a term would appear more often in longer documents than shorter ones. Thus, term frequency is often divided by the  the total number of terms in the document as a way of normalization. […]

Inverse Document Frequency (IDF) is a weight indicating how commonly a word is used. The more frequent its usage across documents, the lower its score. The lower the score, the less important the word becomes. For example, the word the appears in almost all English texts and would thus have a very low IDF score as it carries very little “topic” information. In contrast, if you take the word coffee, while it is common, it’s not used as widely as […]

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not  preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different techniques of text preprocessing and a way to estimate how much preprocessing […]

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 differences between the two and some guidelines on what to use when. Tfidftransformer Usage 1. Dataset and Imports Below we have 5 toy documents, all […]

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 you can hit the ground running without the need for finding a large text corpora which you will have to preprocess and train with 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! Spam classification Other than spam detection, text classifiers can be used to determine sentiment in social media texts, predict categories of news articles, parse and segment unstructured documents, flag the highly talked about fake news articles and more. Text classifiers […]

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 different ways you can use CountVectorizer such that you are not just computing counts of words, but also preprocessing your text data appropriately as well […]

Companies receive support inquiries from various channels. This may include emails, support tickets, tweets, chat conversations with customer support representatives (CSRs), chatbot conversations, and more. Sources of customer service requests This is a lot of data that you are dealing with and it’s mostly unstructured and scattered in nature, making it that much harder to manage. All this text data can actually be leveraged to improve speed in responding to customer service inquiries and reduce the volume of incoming tickets.  According to a research […]

Most text classification examples that you see on the Web or in books focus on demonstrating techniques. This will help you build a pseudo usable prototype. If you want to take your classifier to the next level and use it within a product or service workflow, then there are things you need to do from day one to make this a reality. I’ve seen classifiers failing miserably and being replaced with off the shelf solutions  because they don’t work in […]