"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.
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.
Text Classification is the task of assigning the right label to a given piece of text. This text can either be a phrase, a sentence or even a paragraph. Our aim would be to take in some text as input and attach or assign a label to it. Since we will be using Tensorflow deep learning library, we can call this the Tensorflow text classification system. This task involves training a neural network with lots of data indicating what a piece of text represents.
We'll have our model classify Stack Overflow posts from the top 20 tags. First we'll use Pandas to read our CSV file of training data: When feeding data into our model, we'll separate it into training and test data. The number of rows in our input data will be the number of posts we're feeding the model at each training step (called batch size), and the number of columns will be the size of our vocabulary. For metrics we'll evaluate accuracy, which will tell us the percentage of comments it assigned the correct label to: To train our model, we'll call the fit() method, pass it our training data and labels, the number of examples to process in each batch (batch size), how many times the model should train on our entire dataset (epochs), and the validation split.
What are the advantages of different classification algorithms? For instance, if we have large training data set with approx more than 10,000 instances and more than 100,000 features, then which classifier will be best to choose for classification? The good news though is, that as many problems in life, you can address this question by following the Occam's Razor principle: use the least complicated algorithm that can address your needs and only go for something more complicated if strictly necessary. To read the full article (posted as a Quora question, including 22 answers), click here.
Then the words need to be encoded as integers or floating point values for use as input to a machine learning algorithm, called feature extraction (or vectorization). Running this example prints the array version of the encoded sparse vector showing one occurrence of the one word in the vocab and the other word not in the vocab completely ignored. The TfidfVectorizer will tokenize documents, learn the vocabulary and inverse document frequency weightings, and allow you to encode new documents. A vocabulary of 8 words is learned from the documents and each word is assigned a unique integer index in the output vector.
In binary relevance, this problem is broken into 4 different single class classification problems as shown in the figure below. This function calculates subset accuracy meaning the predicted set of labels should exactly match with the true set of labels. In classifier chains, this problem would be transformed into 4 different single label problems, just like shown below. Scikit-Multilearn library provides different ensembling classification functions, which you can use for obtaining better results.
Encoder is structured similar to Text Classification model, it reads token by token input sequence using RNN cell. After input sequence is finished (" DONE " token in used to indicate that to the model), Decoder starts processing: producing output tokens one by one. Plain RNN decoder would just take output of the Encoder step and on each RNN step, taking previous [correct or decided by the model] token and hidden state of RNN to produce next token. Attention decoder doesn't just take hidden state of RNN and previous token but also uses hidden state of the decoder RNN to "attend" -- select information from encoder output states.
Regression models estimate numerical variables a.k.a dependent variables. Regression models estimate numerical variables a.k.a dependent variables. The features of income and credit rating determines potential defaulters. The classifier learns based on the input features (income and credit ratings) of data.