This is a basic tutorial series for using the Gavagai API v2.
Basic Concepts of
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Monitor API
To be able to use the Gavagai Monitor API you need to familiarize yourself with some of the basic concepts of Gavagai Monitor API. This will make it easier to understand what you can accomplish with the API. Once you understand the basics it will be easy to understand the API documentation which is the key to creating clients using the API.
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Let's start by revisiting a concept that we introduced in an earlier part of the tutorial: the observer (AKA target). This is an important concept in Gavagai APIMonitor API. It is at the focus of our interest and much of the information that is available through the API is tied to the observer. Think of an observer as you would think of the main character of a movie or the brand featured in a commercial. It could be a person, place, brand, or something more abstract like flu symptoms or the name of a country. You describe your target with a set of terms. For example, if your target is Obama your target terms would be "president obama", "barack obama", "obama", etc.
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So we are following a target. What happens then? The Gavagai API Monitor API system receives documents (blog entries, tweets, newspaper articles, and so on) that are published on the internet. Some of these are about our target of interest Obama and "Mentions" is a way of counting those documents. This is a pretty basic measurement but it still has an important place since it can give a general sense of how important a target is over time. It can also serve to normalize other values (more on this later).
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What is the attitude towards Obama in a newspaper article written in the editorial section of a newspaper? What was the general opinion towards Obama in social media over the last month? This is sentiment, and it can be detected and measured by Gavagai APIMonitor API. Every sentence in every article on every webpage that Gavagai API Monitor API receives is being analyzed for its sentiment. All of these values are then aggregated and processed in different ways depending on the type of the sentiment. Is the attitude positive or negative or does it indicate worry or fear? Gavagai API Monitor API can measure any type of sentiment and you get the results as time series aggregated in orderly blocks of hours, days, or months.
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Sentiment measures the attitudes in texts. Associations show us what the texts are about. Associations are the terms that are most strongly associated with our target in the given set of texts. For Obama, some of the more usual associations are (of course) president, senate, committee, barack and so on. The more common an association is to a target the more stable it is considered by Gavagai APIMonitor API. Associations which are not common over long periods of time, but rather pop up unexpectedly, perhaps as some event unfolds, or some significant piece of news is published, are called trending, in Gavagai APIMonitor API. Trending associations are usually short lived and come and go. Stable associations can be a permanent part of a target (like "president" for Obama).
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These are the basic concepts of Gavagai APIMonitor API. There are several others but we will get to them in a later part of the tutorial.
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