Language Processing

Summary

Unstructured text such as emails, support tickets, social media feeds, articles, and product reviews, contain valuable information about each organization.  Machine learning language processing techniques (NLP) have proven to be extremely effective in analyzing text and extract vital information such as meaning, context, keywords, and sentiment. 

Features:

Detect Sentiment
Determine the tone of a textual segment (e.g. positive, negative, indifferent, . . .).  This can be customer emails, product reviews, articles among many other cases

Identify Key Entities
Identify textual entities such as places, names, events, keywords, activities and dates

Determine Document Topic
Evaluate a document and determine its topic

Language Identification
Determine the language of a document

Examples:

Analyze large collection of articles and categorize them based on selected topics and key words.  Deliver only relevant content to users based on their preferences

Sift through large collection of annual reports and extract paragraphs addressing a certain topic

Analyze product reviews and categorize them based on their sentiment

Monitor social media posts and detect ensuing trends

Scan stream of news releases  and identify ones fitting a given criteria