Sentiment Analysis: Can a Monkey Can Do It?

June 27, 2019

Sentiment analysis is a machine learning tool companies are employing to understand how their customers feel about their services and products. It is mainly deployed on social media platforms, including Facebook, Instagram, and Twitter. The Monkey Learn blog details how sentiment analysis is specifically being used on Twitter in the post, “Sentiment Analysis Of Twitter.”

Using sentiment analysis is not a new phenomenon, but there are still individuals unaware of the possible power at their fingertips. Monkey Learn specializes in customer machine learning solutions that include intent, keywords, and, of course, sentiment analysis. The post is a guide on the basics of sentiment analysis: what it is, how it works, and real life examples. Monkey Learn defines sentiment analysis as:

Sentiment analysis (a.k.a opinion mining) is the automated process of identifying and extracting the subjective information that underlies a text. This can be either an opinion, a judgment, or a feeling about a particular topic or subject. The most common type of sentiment analysis is called ‘polarity detection’ and consists in classifying a statement as ‘positive’, ‘negative’ or ‘neutral’.”

It also relies on natural language processing (NLP) to understand the information’s context.

Monkey Learn explains that sentiment analysis is important because most of the world’s digital data is unstructured. Machine learning with NLP’s assistance can quickly sort large data sets and detect their polarity. Monkey Learn promises with their sentiment analysis to bring their customers scalability, consistent criteria, and real-time analysis. Many companies are using Twitter sentiment analysis for customer service, brand monitoring, market research, and political campaigns.

The article is basically a promotional piece for Monkey Learn, but it does work as a starting guide for sentiment analysis.

Whitney Grace, June 27, 2019

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