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In this article, we are going to see how we can easily integrate a Sentiment Analysis engine in your project and how to choose and access the right engine according to your data.
Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
The origin of sentiment analysis can be traced to the 1950s, when sentiment analysis was primarily used on written paper documents.
Sentiment analysis engines appeared in the early 2000s and became increasingly popular due to the abundance of data from social networks, especially those provided by Twitter.
Today, however, sentiment analysis is widely used to mine subjective information from content on the Internet, including texts, tweets, blogs, social media, news articles, reviews, and comments.
You can use Sentiment Analysis in numerous fields, here are some examples of common use cases:
When you need a Sentiment Analysis engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Sentiment Analysis. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Sentiment Analysis engines with a provider as a simple parameter. With only a few lines, you can set up your project in production:
Test and API:
Here is the code in Python (GitHub repo) that allows to test Eden AI for face detection:
Eden AI also allows you to compare these engines directly on the web interface without having to code:
There are numerous Sentiment Analysis engines available on the market: it is impossible to know all of them, to know those who provide good performance. The best way you have to integrate Sentiment Analysis technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
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