"A text classifier is an automated means of determining some metadata about a document. Text classifiers are used for such diverse needs as spam filtering, suggesting categories for indexing a document created in a content management system, or automatically sorting help desk requests."
– John Graham-Cumming, Naive Bayesian Text Classification. Dr. Dobb's. May 1 2005.
In binary classification problems such as predicting probability of churn or probability of default, it makes sense to have a time-series dataset (multiple observations for each account, with each observation representing a unique point-in-time). I imagine this dataset structure violates standard logistic regression, but could more advanced regression forms (such as generalized linear mixed models) or tree-based algorithms handle this type of dataset structure?
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.
How to lean machine learning in python? And what is transfer learning? How to create a sentiment classification algorithm in python? In the world of today and especially tomorrow machine learning will be the driving force of the economy. No matter who you are, an entrepreneur or an employee, and in which industry you are working in, machine learning will be on your agenda.
Even though the most online review systems offer star rating in addition to free text reviews, this only applies to the overall review. However, different users may have different preferences in relation to different aspects of a product or a service and may struggle to extract relevant information from a massive amount of consumer reviews available online. In this paper, we present a framework for extracting prevalent topics from online reviews and automatically rating them on a 5-star scale. It consists of five modules, including linguistic pre-processing, topic modelling, text classification, sentiment analysis, and rating. Topic modelling is used to extract prevalent topics, which are then used to classify individual sentences against these topics.
Everyone is talking about training the Deep Learning models and fine tuning them but very few talks about the deployment and the scalability aspects. In BotSupply, we focus not only on building accurate Machine Learning models, but also on delivering them to the clients with the greater efficiency. In this article, we will learn to deploy a sentiment analysis model trained on "Character-level Convolutional Networks for Text Classification" (Xiang Zhang, Junbo Zhao, Yann LeCun) which uses character-level ConvNet networks for text classification. Check out his great blog post on CNN classification. As explained in the above blog about the training process, I am pre-assuming that you have already trained your sentiment analysis model.
Kim 2014 and Collobert 2011 argue that max-over-time pooling helps getting the words from a sentence that are most important to the semantics. Then I read a blog post from the Googler Lakshmanan V on text classification. The author argues that spatial invariance isn't wanted because it's important where words are placed in a sentence. Thus he doesn't recommend maxpool. Are there empirical studies that compares the two approaches?
You'll work with the IMDB dataset: a set of 50,000 highly polarized reviews from the Internet Movie Database. They're split into 25,000 reviews for training and 25,000 reviews for testing, each set consisting of 50% negative and 50% positive reviews. Because you should never test a machine-learning model on the same data that you used to train it! Just because a model performs well on its training data doesn't mean it will perform well on data it has never seen; and what you care about is your model's performance on new data (because you already know the labels of your training data – obviously you don't need your model to predict those). For instance, it's possible that your model could end up merely memorizing a mapping between your training samples and their targets, which would be useless for the task of predicting targets for data the model has never seen before.
The second part was… a lot more difficult. To acquire the real news side of the dataset, I turned to All Sides, a website dedicated to hosting news and opinion articles from across the political spectrum. Articles on the website are categorized by topic (environment, economy, abortion, etc…) and by political leaning (left, center, and right). I used All Sides because it was the best way to web scrape thousands of articles from numerous media outlets of differing biases. Plus, it allowed to me download the full text of an article, something you cannot do with the New York Times and NPR APIs.
How can I predict my customer base? In this webinar, we'll answer real data science questions like this using Spotfire and TERR to make smarter decisions. For our next webinar, we'll be managing a hotel's marketing group, using classification methods inside of Spotfire. This is the fourth step in our five-part webinar series called the Building Blocks of Data Science. In this series, we will explore solving real data science questions using Spotfire and TERR.