Multiple Disparate Data Sources

• All the text data sources have to be identified
• Text data source maybe:
  • web pages scrapped or crawled over the internet
  • databases for various systems and interrelationships
  • data warehouses
  • text files or logs
  • live incoming text information e.g. live chat
  • combination of above

Aggregation

• Data from all these sources is aggregated for further processing
• This usually also involves converting the data into a uniform format  homogenization
• The exclusive microservices-based horizontally scalable system may be dedicated for storage and serving data e.g. Kafka

Feature Engineering

• Based on the goal, relevant and interesting data is extracted at this stage
• Usually, for text, you may end up collecting words or group of words in a Bag Of Words
• Some NLP may be used for selection of interesting words and weights may have to be decided e.g. sentiment scores
• Words are grouped and marked with labels for a machine to be able to learn from

Machine Learning

• Based on the problem statement, appropriate machine learning algorithms are applied to build a model
• Learning may be achieved by processing the features having weights or maybe as simple as word count.
• At this stage, libraries like scikit, NumPy, pandas, TensorFlow, theano, etc. come in handy
• The learning process can be actively monitored via graphs and intuition built to tackle problems like – scaling, overfitting, underfitting, improper learning rate, etc.
• Validation is also carried out to determine the accuracy of the model

Prediction

• After learning, the model is ready for prediction
• Model itself can be stored as binary (TensorFlow) that can be re-loaded and used for predictions in the future

Small Sample Project

Predict the author from the given some text snippet. As a sample, we are using Enron’s email text corpus and learning patterns to predict the author.