Machine Learning For Dummies

Chapter 20

Scoring Opinions and Sentiments

IN THIS CHAPTER

Considering Natural Language Processing (NLP)

Defining how machines can understand text

Performing scoring and classification tasks

Many people have the idea that somehow computers can understand text. The fact is that computers don’t even have a way in which to represent text — it’s all numbers to the computer. This chapter helps you understand three phases of working with text to score opinions and sentiments: using Natural Language Processing (NLP) to parse the text; performing the actual task of understanding the text; and then performing scoring and classification tasks to interact with the text meaningfully.

Introducing Natural Language Processing

As human beings, understanding language is one our first achievements, and associating words to their meaning seems natural. It’s also automatic to handle discourses that are ambiguous, unclear, or simply have a strong reference to the context of where we live or work (such as dialect, jargon, or terms family or associates understand). In addition, humans can catch subtle references to feelings and sentiments in text, enabling people to understand polite speech that hides negative feelings and irony. Computers don’t have this ability but can rely on NLP, a field of computer science concerned with language understanding and language generation between a machine and a human being. Since Alan Turing first devised the Turing Test in 1950, which aims at spotting an artificial ...