Which Sentiment Analysis API to choose for your project?
Tags: sentimentanalysis, api
Published: 2022-08-05 13:57:00

_This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at: contact@edenai.co_

## Intro:

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.


## Definition:

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.

## History:

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.

## Use cases:

You can use Sentiment Analysis in numerous fields, here are some examples of common use cases:

- healthcare: analyze satisfaction surveys to identify negative poitns and improve hospitals
- banking: analyze text from social media campaigns to make sure that its customers did not turn to other banks.
- call centers: identify negative interactions in calls, to analyze it with other NLP techniques
- retail: analyze after-sales service requests to identify those which must be treated

### The Multi cloud approach

When you need a Sentiment Analysis engine, you have 2 options:

- First option: multiple open source Sentiment Analysis engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use. Using an open source AI library requires data science expertise. Moreover, you will need to set up a server internally to run open source engines.
- Second option: you can use engines from your cloud provider. Actually, cloud providers like Google Cloud, AWS, Microsoft Azure, Alibaba Cloud or IBM Watson are all providing multiple AI engines including Sentiment Analysis. This option looks very easy because you can stay in a known environment where you might have abilities in your company and the engine is ready-to-use.
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:

- you may not know every performant providers on the market
- you need to subscribe and contract with all providers
- you need to master each providers API documentation
- you need to check their pricings
- You need to process data in each engine to realize the benchmark

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:

{% embed https://www.youtube.com/watch?v=GiSyGU98cTo&feature=emb_title %}

## Test and API:

Here is the code in Python (GitHub repo) that allows to test Eden AI for face detection:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2029ltm3x4n4lsia0guf.png) 
Eden AI SDK for Sentiment Analysis

### Answer:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qk5ouw55c4l6nb2ivutf.png) 
Eden AI SDK for Sentiment Analysis

### Platform:

Eden AI also allows you to compare these engines directly on the web interface without having to code:

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/dp62texrsryozlz513h5.png) 
Eden AI Platform for Sentiment Analysis

## Conclusion:

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.