What is sentiment analysis? Using NLP and ML to extract meaning

Sentiment analysis defined

Sentiment analysis is analytical technique that uses statistics,  natural language processing, and machine learning to determine the emotional meaning of communications.

Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services.

Sentiment analysis examples

One of the most prominent examples of sentiment analysis on the Web today is the  Hedonometer, a project of the University of Vermont’s Computational Story Lab.

The group analyzes more than 50 million English-language tweets every single day, about a tenth of Twitter’s total traffic, to calculate a daily happiness store.

The approach is computationally straightforward. The lab collected a set of 10,000 commonly used words and, via Amazon’s Mechanical Turk service, had people give each word a happiness score of one to nine. Neutral words, and words that depend heavily on context, are filtered out, and the scores of the rest are added up and averaged to determine daily happiness scores. The word lists, with scores, are available on the project’s website in English and nine other languages.

This “bag of words” approach is an old-school way to perform sentiment analysis, says Hayley Sutherland, senior research analyst for conversational AI and intelligent knowledge discovery at IDC. “But it can be great for really large sets of text,” she says.

Read full article at CIO magazine.