Text Classification


Multi-Class Text Classification with Scikit-Learn – Towards Data Science

#artificialintelligence

There are lots of applications of text classification in the commercial world. For example, news stories are typically organized by topics; content or products are often tagged by categories; users can be classified into cohorts based on how they talk about a product or brand online … However, the ...


How Machine Learning, Classification Models Impact Marketing Ethics - InformationWeek

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People seek convenience in their experiences with brands. Brands have begun to use machine learning classification to know who, where, and when to direct resources to provide that convenience. But in relying on algorithms to provide customer convenience, managers must understand classification to p...


Too Many Secrets

Slate

Last week's decision by House Intelligence Committee Republicans and the White House to declassify a misleading, politically charged memo about evidence in the Russia investigation is yet another example of our toxic political environment. But it also points to another problem--one that existed long...


The Best Metric to Measure Accuracy of Classification Models

@machinelearnbot

Unlike evaluating the accuracy of models that predict a continuous or discrete dependent variable like Linear Regression models, evaluating the accuracy of a classification model could be more complex and time-consuming. Before measuring the accuracy of classification models, an analyst would first measure its robustness with the help of metrics such as AIC-BIC, AUC-ROC, AUC- PR, Kolmogorov-Smirnov chart, etc. The next logical step is to measure its accuracy. To understand the complexity behind measuring the accuracy, we need to know few basic concepts. E.g. – A classification model like Logistic Regression will output a probability number between 0 and 1 instead of the desired output of actual target variable like Yes/No, etc.


How Machine Learning, Classification Models Impact Marketing Ethics - Pierre DeBois @allanalytics

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But in relying on algorithms to provide customer convenience, managers must understand classification to protect brands from making unethical societal choices when delivering outcomes to customers. It's not new for businesses to be proactive in an effort to influence. History is filled with intriguing stories of worthy trials such as constructing homes near plants for workers, to failed efforts, too, such as those that led to the 2009 global financial crisis. When businesses use technology to influence, an important question arises. What qualities become associated with algorithms?


[P] Build a text classification model without any training data • r/MachineLearning

@machinelearnbot

Imagine predicting the emotion of a tweet without providing any training examples of tweets with that emotion label.This research discusses the paradigm of Zero-shot learning for Text Classification and the paper is aptly titled as "Train Once, Test Anywhere: Zero-shot Learning For Text Classification". You can read the paper here or try a demo here.


Automated Text Classification Using Machine Learning

@machinelearnbot

Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to create, analyze and report information fast. This is when automated text classification steps up.


Setting up the target variable in a classification problem

@machinelearnbot

Now since I intend to predict all employees who will churn in the next 6 months, we could tag all exits between Apr'17 to Sep'17 as 1 (have churned) and all other exits (Jan'17 to Mar'17) as active. In this case, only the people who churned in the last 6 months are tagged as inactive and the rest, who may have churned, are still tagged active. Here I bring in time interval in predictions. Is this is a better way, or do I need to approach the problem in a yet different manner?


Automated Text Classification Using Machine Learning

#artificialintelligence

Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to create, analyze and report information fast. This is when automated text classification steps up.


Machine Learning: Classification Models – Fuzz – Medium

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These days the terms "AI", "Machine Learning", "Deep Learning" are thrown around by companies in every industry, they're the type of words that make any forward-looking executive salivate. You might think these are new concepts that seemed to have appeared overnight, but the reality is they've been around for a while and it's the hard work of many within the field that has really moved it into the spotlight as the latest tech trend. While these terms are sometimes used interchangeably by the media they certainly are not the same, but I'll leave that discussion for another time. It's surely an exciting time for the industry, from a slew of open source libraries (TenserFlow, PredictionIO, DeepLearning4J, or see github) coming into popularity and every cloud provider from Amazon, IBM, Microsoft (the list goes on) all offering their own tools to help get started in the AI/ML/DL field. If you've stumbled on this article, you're probably well aware of everything I've mentioned above, so now that we've gotten past the obligatory intro, let's get to what the title actually claims this article is about.