Classification, or categorization, is the task of assigning labels to various items, such as products. Classification happens effortlessly in humans' everyday lives. Imagine, for example, that you are going to the grocery store. There, you will implicitly assign labels to the products such as "healthy" versus "not healthy", "GMO" versus "non GMO", or "fresh" versus "stale".
Text classification is the task of assigning labels to text documents. Documents can be webpages, emails, advertisements, or even product reviews. Here are some examples of text classification: categorizing a web page as "English language" versus "Chinese language" versus "other language", an email as "spam" versus "not spam", or a product review as "positive" versus "negative". Since the number of the existing documents is already huge and it is growing rapidly every day, it is impossible to ask humans to manually classify every document. As a result, we need techniques that can automatically assign labels to text.
In order to better understand how text classifiers work, let’s think about how people classify items. Back to the grocery store example, imagine that we want to classify an item as "healthy" versus "not healthy". In order to make the decision, we will first identify a set of item features that is important for the classification, (e.g., percentage of sugar, fat, salt). Then, we will extract those features from the actual product (e.g., 10% sugar, 1% of fat, and 0.1% of salt). Finally, we will combine the values of the features in some way and if they exceed a specific threshold we will classify the product as "healthy" or "not healthy".
A spam detection classifier that labels an email as "spam" or "not spam" works in a similar way: it accepts as input a set of emails, which have already been labeled as "spam" or "not spam", and extracts features, such as the domain of the sender (e.g., the country), or the appearance of links and images in the email. Text classification also benefits from features such as the words or phrases in the email. For example, spam emails usually contain phrases such as "free i-phone" or "your credit card info is needed". The classifier uses the documents with the known labels and learns a model that, given the values of the features, can classify new incoming emails as "spam" or "not spam".
Although there is a large body of research in the text classification domain, there is a growing need for developing new text classification models that are able to successfully distinguish the label of documents. This is due to reasons such as the arrival of the social networks, the evolution of the writing style, or the appearance of new formats of textual information (e.g., emoji). Recent research has witnessed much success in automatic text classification due to the advent of deep learning, but the problem continues to be a challenging one in the Artificial Intelligence community.
- Pigi Kouki
Today the data science community is still lacking good practices for organizing their projects and effectively collaborating. ML algorithms and methods are no longer simple "tribal knowledge" but are still difficult to implement, manage and reuse. To address the reproducibility we have build Data Version Control or DVC. This example shows you how to solve a text classification problem using the DVC tool. Git branches should beautifully reflect the non-linear structure common to the ML process, where each hypotheses can be presented as a Git branch. However, inability to store data in a repository and the discrepancy between code and data make it extremely difficult to manage a data science project with Git.
Machine learning generates a lot of buzz because it's applicable across such a wide variety of use cases. That's because machine learning is actually a set of many different methods that are each uniquely suited to answering diverse questions about a business. To better understand machine learning algorithms, it's helpful to separate them into groups based on how they work.
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. In contrast to the existing text moderation service that flags profanity terms, the text classification feature helps detect potentially undesired content that may be deemed as inappropriate depending on context. In addition, to convey the likelihood of each category it may recommend a human review of the content. The text classification feature is in preview and supports the English language.
Apache Spark is quickly gaining steam both in the headlines and real-world adoption, mainly because of its ability to process streaming data. With so much data being processed on a daily basis, it has become essential for us to be able to stream and analyze it in real time. In addition, Apache Spark is fast enough to perform exploratory queries without sampling. Many industry experts have provided all the reasons why you should use Spark for Machine Learning? So, here we are now, using Spark Machine Learning Library to solve a multi-class text classification problem, in particular, PySpark.
From climate change to opioid addiction, we are facing serious public health crises that put our research and data management experts to the test. When it comes to scientific evidence, systematic literature reviews--painstaking assessments of all the literature ever produced on a given subject--are often regarded as the gold standard. Though no research method is foolproof, says Vox health correspondent Julia Belluz, "these studies represent the best available syntheses of global evidence about the likely effects of different decisions, therapies and policies." That comprehensiveness comes at high price, though, in terms of time and money. It involves sifting through enormous volumes of literature--sometimes hundreds of thousands of scientific abstracts--stored in academic databases.
We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using the command sudo apt-get install awscam on the Ubuntu terminal. For this tutorial, you need the MXNet version 0.12. You can update the MXNet version by using the command sudo pip3 install mxnet 0.12.1.
Text analysis, as a whole, is an emerging field of study. Fields such as Marketing, Product Management, Academia, and Governance are already leveraging the process of analyzing and extracting information from textual data. We discussed the technology behind Text Classification, one of the essential parts of Text Analysis. Text classification or Text Categorization is the activity of labeling natural language texts with relevant categories from a predefined set. In laymen terms, text classification is a process of extracting generic tags from unstructured text. These generic tags come from a set of pre-defined categories. Classifying your content and products into categories help users to easily search and navigate within website or application.