In this article, we will see how to use machine learning for Text Analysis. Natural language processing is a branch of AI and machine learning that aims at extracting meaning out of text by using machine learning algorithms.
Using machine learning Natural Language Processing methods, you can analyze text intent (also known as Sentiment Analysis), categorize text into desired categories and take much better inferences and decisions using textual data.
Table of Contents
Steps to use machine learning for Text Analysis
1. Organize and label your data
Typically the bigger your organization is, the larger the size of text data sets you will have to deal with. Make sure that the data you have is well organized for consumption. For example, if you are dealing with a list of Facebook comments, you will need to categorize them as spammy or not spammy based. This is called labeling and will help in producing better accuracy in the machine learning process.
2. Text Cleansing
You must clean your text first, which means splitting it into words and handling punctuation and case. Cleaning your data is important. This will save resources and time for the machine learning process. For example, you can remove signs, symbols, punctuation like commas, apostrophes, quotes, question marks, and more. You can use a tool like NLTK (The Natural Language ToolKit) for text cleaning.
Steps in Text cleansing:
Split into Sentences
Split into Words
Remove Stop Words
Machines deal with numbers not text. So it is important to convert text to machine readable formats called vectors. You can use the two following methods of vectorization.
Continuous Bag-of-Words Model
Continuous Skip-gram Model
Text classification through Machine Learning
(Image source: towards data science)
4. Text Classification
Text classification can either be done by a human or by machines. The human method is time consuming whereas the automatic method uses machine learning techniques to classify data. Here are common text classification methods.
Supervised Latent Dirichlet Allocation (SLDA)
Support Vector Machines (SVM)
Multinomial Logistic Regression (maximum entropy)
Naive Bayes (see also multinomial NB)
Boosting and Bagging algorithms
Once your machine learning model is built, you can optimize your model using an error matrix which shows the delta between your predictions and the actual output values. Such optimizations can help in building a robust machine learning model that handles text effectively.
Skyl.ai provides Content Categorization solution which allows a huge amount of text, image, audio, and video content to be categorized into predefined sections and topics. Here is more information on Skyl.ai's categorization solutions.
Check out the various solutions that can be built using Sky.ai Platform here.