AI News, Automated Text Classification Using Machine Learning

Automated Text Classification Using Machine Learning

Digitization has changed the way we process and analyze information.

From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data.

And, using machine learning to automate these tasks, just makes the whole process super-fast and efficient.

As Jeff Bezos said in his annual shareholder’s letter, Over the past decades, computers have broadly automated tasks that programmers could describe with clear rules and algorithms.

In this post, we talk about the technology, applications, customization, and segmentation related to our automated text classification API.

During the testing phase, the algorithm is fed with unobserved data and classifies them into categories based on the training phase.

It can operate for special use cases such as identifying emergency situation by analyzing millions of online information.

To identify emergency situation among millions of online conversation, the classifier has to be trained with high accuracy.

It needs special loss functions, sampling at training time and methods like building a stack of multiple classifiers each refining the results of previous one to solve this problem.

The algorithms are given a set of tagged/categorized text (also called train set) based on which they generate AI models, these models when further given the new untagged text, can automatically classify them.

The image below shows the nearest neighbors of the tweet “reliance jio prime membership at rs 99 : here’s how to get rs 100 cashback…”.

Automated Text Classification Using Machine Learning

Digitization has changed the way we process and analyze information.

From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data.

And, using machine learning to automate these tasks, just makes the whole process super-fast and efficient.

As Jeff Bezos said in his annual shareholder’s letter, Talking particularly about automated text classification, we have already written about the technology behind it and its applications.

During the testing phase, the algorithm is fed with unobserved data and classifies them into categories based on the training phase.

It needs special loss functions, sampling at training time and methods like building a stack of multiple classifiers each refining the results of previous one to solve this problem.

The algorithms are given a set of tagged/categorized text (also called train set) based on which they generate AI models, these models when further given the new untagged text, can automatically classify them.

The image below shows the nearest neighbors of the tweet “reliance jio prime membership at rs 99 : here’s how to get rs 100 cashback…”.

There are many people who want to use AI for categorizing data but that needs making a data-set giving rise to a situation similar to a chicken-egg problem.

In ParallelDots’ latest research work, we have proposed a method to do zero-shot learning on text, where an algorithm trained to learn relationships between sentences and their categories on a large noisy dataset can be made to generalize to new categories or even new datasets.

We also propose multiple neural network algorithms that can take advantage of this training methodology and get good results on different datasets.

The idea is if one can model the concept of “belongingness” between sentences and classes, the knowledge is useful for unseen classes or even unseen datasets.

We also believe that it will bring down the threshold of building practical machine learning models that can applied across industries solving a variety of use-cases.

As more and more information is dumped on the internet, it is up to the intelligent machine algorithms to make analyzing and representing this information easily.

Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow

A neural network is a computational model (a way to describe a system using mathematical language and mathematical concepts).

Every node has a weight value, and during the training phase the neural network adjusts these values in order to produce a correct output (wait, we will learn more about this in a minute).

This function is defined this way: f(x) = max(0,x) [the output is x or 0 (zero), whichever is larger] Examples: ifx = -1, then f(x) = 0(zero);

Hidden layer 2 The 2nd hidden layer does exactly what the 1st hidden layer does, but now the input of the 2nd hidden layer is the output of the 1st one.

For example, if we want to encode three categories (sports, space and computer graphics): So the number of output nodes is the number of classes of the input dataset.

This function transforms the output of each unity to a value between 0 and 1 and also makes sure that the sum of all units equals 1.

Translating everything we saw so far into code, the result is: (We’ll talk about the code for the output layer activation function later.) As we saw earlier the weight values are updated while the network is trained.

When we run the network for the first time (that is, the weight values are the ones defined by the normal distribution): To know if the network is learning or not, you need to compare the output values (z) with the expected values (expected).

With TensorFlow you will compute the cross-entropy error using the tf.nn.softmax_cross_entropy_with_logits() method (here is the softmax activation function) and calculate the mean error (tf.reduce_mean()).

You want to find the best values for the weights and biases in order to minimize the output error (the difference between the value we got and the correct value).

Multi-Class Text Classification with Scikit-Learn

users can be classified into cohorts based on how they talk about a product or brand online … However, the vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering (spam vs.

The problem is supervised text classification problem, and our goal is to investigate which supervised machine learning methods are best suited to solve it.

Before diving into training machine learning models, we should look at some examples first and the number of complaints in each class: For this project, we need only two columns — “Product” and “Consumer complaint narrative”.

Example: “ I have outdated information on my credit report that I have previously disputed that has yet to be removed this information is more then seven years old and does not meet credit reporting requirements” Example: Credit reporting We will remove missing values in “Consumer complaints narrative” column, and add a column encoding the product as an integer because categorical variables are often better represented by integers than strings.

For some cases, such as fraud detection or cancer prediction, we would need to carefully configure our model or artificially balance the dataset, for example by undersampling or oversampling each class.

The classifiers and learning algorithms can not directly process the text documents in their original form, as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.

One common approach for extracting features from text is to use the bag of words model: a model where for each document, a complaint narrative in our case, the presence (and often the frequency) of words is taken into consideration, but the order in which they occur is ignored.

We will use sklearn.feature_extraction.text.TfidfVectorizer to calculate a tf-idf vector for each of consumer complaint narratives: (4569, 12633) Now, each of 4569 consumer complaint narratives is represented by 12633 features, representing the tf-idf score for different unigrams and bigrams.

We will benchmark the following four models: model_nameLinearSVC: 0.822890LogisticRegression: 0.792927MultinomialNB: 0.688519RandomForestClassifier: 0.443826Name: accuracy, dtype: float64 LinearSVC and Logistic Regression perform better than the other two classifiers, with LinearSVC having a slight advantage with a median accuracy of around 82%.

However, there are a number of misclassifications, and it might be interesting to see what those are caused by: As you can see, some of the misclassified complaints are complaints that touch on more than one subjects (for example, complaints involving both credit card and credit report).

Text Classification using Neural Networks

We’ll use 2 layers of neurons (1 hidden layer) and a “bag of words” approach to organizing our training data.

While the algorithmic approach using Multinomial Naive Bayes is surprisingly effective, it suffers from 3 fundamental flaws: As with its ‘Naive’ counterpart, this classifier isn’t attempting to understand the meaning of a sentence, it’s trying to classify it.

We will take the following steps: The code is here, we’re using iPython notebook which is a super productive way of working on data science projects.

The above step is a classic in text classification: each training sentence is reduced to an array of 0’s and 1’s against the array of unique words in the corpus.

We are now ready to build our neural network model, we will save this as a json structure to represent our synaptic weights.

This parameter helps our error adjustment find the lowest error rate: synapse_0 += alpha * synapse_0_weight_update We use 20 neurons in our hidden layer, you can adjust this easily.

These parameters will vary depending on the dimensions and shape of your training data, tune them down to ~10^-3 as a reasonable error rate.

low-probability classification is easily shown by providing a sentence where ‘a’ (common word) is the only match, for example: Here you have a fundamental piece of machinery for building a chat-bot, capable of handling a large # of classes (‘intents’) and suitable for classes with limited or extensive training data (‘patterns’).

Train an Image Classifier with TensorFlow for Poets - Machine Learning Recipes #6

Monet or Picasso? In this episode, we'll train our own image classifier, using TensorFlow for Poets. Along the way, I'll introduce Deep Learning, and add context ...

Text Classification Using Naive Bayes

This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.

Text Classification - Natural Language Processing With Python and NLTK p.11

Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is ...

Train/Test Split in sklearn - Intro to Machine Learning

This video is part of an online course, Intro to Machine Learning. Check out the course here: This course was designed ..

How to Make a Text Summarizer - Intro to Deep Learning #10

I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, ...

Python for Machine Learning - Part 18 - Sampling - Train Test Split

Topic to be covered : Sampling using Train_Test_Split from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test ...

Weka Text Classification for First Time & Beginner Users

59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis ...

Classification using Pandas and Scikit-Learn

Skipper Seabold This will be a tutorial-style talk demonstrating how to use ..

Regression Training and Testing - Practical Machine Learning Tutorial with Python p.4

Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit ...

How to Build a Text Mining, Machine Learning Document Classification System in R!

We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to ...