In this guide, we will be covering how to use the Sentiment Analysis API available through IBM Watson’s AlchemyLanguage API collection.
We will walk through how to get started, how to request data, and how to handle the reponse. At the end, we even provide a demo for you to try conducting your own Sentiment Analysis on a piece of text.
Before we dive in, let's learn a bit more about the specific tools we will be using:
IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. Unlike conventional approaches to computing, which can only handle structured data, such as that stored in a database, Watson can understand unstructured data. Unstructured data includes blog posts, articles, reviews, and social media posts, and accounts for much of the data on the Internet today.
IBM Watson contains several natural language processing APIs available through AlchemyLanguage - a collection of APIs that offer text analysis through natural language processing. The AlchemyLanguage APIs analyze text in order to glean high-level semantic information, such as sentiment, keywords, entities, high-level concepts, and more. One such API is the Sentiment Analysis API.
Sentiment Analysis is an API that processes text and indicates whether the expressed opinion in that text is positive, negative, or neutral. By returning the polarity along with an associated score, we can determine the attitude of an author or speaker with respect to some topic. Sentiment Analysis is often used to detect sentiment from people's digital footprints (i.e. online reviews and social media posts), as such insights can have wide ranging applications in marketing and customer service. In the Demo section of this guide, we will demonstrate how the API is used to perform a Sentiment Analysis on a piece of example text.
Now that we've gotten these definitions out of the way, let's get started.